Ethical Tech Debt – How to manage technology debt in an era of rapid innovation

Technology debt (tech debt) is an unavoidable part of software development, but a responsible approach to its management can benefit not only financially, but also in terms of business ethics. This article discusses the concept of “ethical tech debt” and provides strategies for mitigating the negative effects of design negligence or outdated infrastructure. Learn how to consciously approach the issue of tech debt to balance innovation, code quality and the well-being of users and stakeholders.

Technology debt is a metaphor describing the consequences of adopting inadequate technical solutions in the name of rapid software development. The concept, introduced by Ward Cunningham, compares quality compromises to taking out a financial loan. Developers “borrow time” by choosing faster but less optimal solutions, and then pay “interest” in the form of additional effort needed later for system maintenance and development. With each day that the debt remains unpaid, its cost increases, burdening subsequent iterations of product development.

In the modern context of digital business, we introduce the concept of “ethical technology debt,” which goes beyond the purely technical aspects of the issue. The ethical dimension underscores the responsibility of software developers to a wide range of stakeholders – from end users to colleagues to future generations of engineers who will work with the code we leave behind. An ethical approach to debt management requires full transparency in documenting the compromises consciously made, honest communication of the associated risks, and a consistent effort to systematically reduce the accumulated debt.

Ignoring the ethical dimension of technology debt in today’s business environment leads to a number of concrete, measurable consequences. Organizations experience a decline in customer confidence when products become unstable or inefficient. Their competitive position weakens when they are unable to respond quickly to market changes. Finally, they face serious security risks when debt in data protection areas reaches critical levels. Ethical debt management, therefore, goes well beyond technical problem tracking – it becomes a fundamental component of protecting an organization’s values and securing its future in a digital world.

Key components of ethical technological debt

• Transparency – open communication of existing debt to all stakeholders
• Responsibility – making conscious decisions about incurring and repaying debt
• Balance – balancing speed of delivery with quality
• Intergenerational justice – concern for future developers

How does rapid innovation affect the accumulation of technology debt in IT projects?

Today’s technology environment is characterized by an unprecedented pace of change that puts tremendous pressure on development teams. The market is constantly demanding new functionality, shorter release cycles and faster response to user needs. Faced with these demands, organizations often prioritize speed of delivery at the expense of technical quality. Development teams, compelled to speed things up, opt for solutions that work “here and now” instead of those that are optimal in the long term, leading to a systematic accumulation of technology debt in almost every aspect of the system.

The situation is further complicated by the constant evolution of the technology ecosystem. New frameworks, libraries, programming languages and standards are emerging at a dizzying pace, while systems built on older technologies still need to be maintained and developed. Organizations find themselves in a difficult position, trying to balance modernization with business continuity. Integrating new technologies with existing systems often leads to layers of adaptation, interfaces and workarounds that over time become a source of serious technical debt.

An interesting paradox emerges here – the drive for rapid innovation without proper management of technology debt ultimately leads to the opposite effect. When technical debt reaches a critical level, teams begin to spend more and more time solving problems resulting from past neglect, and less and less time creating new business value. What was supposed to accelerate growth becomes its biggest inhibitor. Organizations get caught in a vicious cycle, where time pressure leads to more debt, which in turn slows further development and increases the pressure to deliver more functionality faster.

Indicators of excessive debt accumulation

• Increasing time to introduce similar functionality
• Growing number of production incidents after deployments
• Declining developer satisfaction and increased turnover in teams
• Difficulties in adapting new technologies

Why should companies treat technology debt as a strategic challenge?

Technology debt has long ceased to be a purely technical problem for development teams – it has become a key determinant of an organization’s ability to successfully execute its business strategy in a dynamic market environment. Companies that ignore mounting technical debt gradually lose operational flexibility and the ability to respond quickly to changing market conditions. IT systems burdened by significant debt become more difficult to modify and expand, which directly translates into increased time to implement changes, increased risk of failure and reduced ability to implement innovations.

The financial consequences of neglect in the area of technology debt are not only measurable, but also significant to a company’s overall performance. In extreme cases, the cost of maintaining outdated and inefficient systems can consume as much as 40% of the total IT budget, systematically crowding out funds allocated to development and innovation initiatives. Organizations also incur hidden costs related to lost revenue resulting from delayed product launches and additional expenses for recruiting and deploying new employees in the face of increased turnover of frustrated developers. What’s more, IT systems burdened by high debt are steadily losing value as assets of the organization, accelerating the need to replace them altogether.

A strategic approach to managing technology debt requires a fundamental change in decision-making processes – these issues must be addressed regularly at the highest levels of the organization. Chief Technology Officers (CTOs) and board members should systematically receive reports on the level of technology debt, analyze its impact on the achievement of key business objectives, and allocate appropriate resources for its systematic reduction. Only by treating technology debt as a strategic element of corporate governance can organizations effectively balance short-term business goals with the long-term sustainability and effectiveness of their IT systems.

Strategic consequences of neglecting technology debt

• Loss of business flexibility and ability to respond quickly to change
• Rising cost of maintaining systems (by 20-30% per year on average)
• Difficulties in attracting and retaining talented developers
• Increased risk of major accidents and security incidents

What ethical risks arise when neglecting technology debt management?

Neglecting to manage technology debt raises serious ethical challenges well beyond the purely technical consequences of code bugs or suboptimal architectural decisions. The most serious of these dilemmas is the systematic transfer of responsibility and burden to future development teams. By leaving undocumented or excessive debt, we are deliberately creating hidden traps for our successors, who will be forced to suffer the consequences of today’s negligence without being able to influence the decisions that led to it. This form of “intergenerational injustice” in the context of software development is a serious violation of the professional ethics of software engineers, the foundation of which is to create value, not to pass problems on to others.

Another important ethical dimension is the issue of transparency to customers and end users. Organizations that knowingly hide technical inadequacies in their systems that can affect security, performance or service availability are actually abusing the trust of users. The problem is particularly acute in the case of consulting and outsourcing companies that provide solutions burdened with significant technology debt without transparently informing customers of the associated risks and future maintenance costs. Such practice undermines the foundations of an honest business relationship and can be seen as a form of deception.

The ethical aspects of treating technical workers forced to work in an environment burdened by high technological debt cannot be overlooked either. Developers permanently engaged in “firefighting” resulting from historical negligence experience increased stress, job burnout and professional frustration. In extreme cases, a toxic work environment leads to a kind of moral burnout – developers lose the motivation to care about quality and begin to consciously lower standards themselves, perpetuating a vicious cycle of neglect. This mechanism of quality culture erosion has far-reaching consequences not only for a given organization, but potentially for the entire IT industry.

Unacceptable practices from an ethical perspective

• Knowingly hiding security problems from users
• Shifting all responsibility for code quality to developers without organizational support
• Ignoring warning signals from technical teams
• Delivering solutions to customers with known high technical debt without transparent communication

How do you identify and prioritize technology debt in existing systems?

Effective identification and management of technology debt requires a multidimensional, systematic approach that includes analysis of both the code level and the overall system architecture. The starting point is usually the introduction of automated static analyses that allow key indicators of technical quality to be objectively measured. Among the most important parameters is cyclomatic complexity, which for well-designed functions should remain below a value of 10 to ensure their testability and readability. An equally important indicator is the level of code duplication, which in a healthy system should not exceed 5%, as well as test coverage, where a minimum of 75% coverage is expected for code implementing key business logic. These analyses should also detect violations of accepted coding standards and identify deprecated dependencies and libraries that could be a source of future security and compatibility problems.

In parallel with code-level analysis, it is necessary to conduct regular architectural reviews to assess the overall state of the system. During such reviews, the consistency of the implementation with the accepted design patterns is verified, and areas of excessive complexity and multiple interdependencies between components are identified. Particular attention should be paid to the phenomenon known as “feature creep,” i.e. the gradual growth of functionality beyond the original design, which often leads to an erosion of architectural purity. An architecture audit should also assess the adequacy of the technologies used to meet current business requirements, identifying components that may have become bottlenecks in system performance or flexibility.

Once the areas burdened by technology debt have been identified, the key challenge becomes their proper prioritization, which should take into account three main dimensions. The first is business impact – the extent to which an element of debt limits the ability to develop value-generating functionality for the organization and its customers should be assessed. The second dimension is the risk associated with leaving the debt unrepaired, taking into account both the likelihood of failure and the potential scale of its consequences. The third factor is the cost of debt reduction, i.e. the estimated amount of work required to fix the identified problems. By skillfully balancing these three perspectives, it is possible to create an effective technology debt reduction plan that will yield the greatest value with optimal use of available resources.

Practical methods for prioritizing technology debt

• 2×2 matrix – evaluation of debt according to two axes: business impact and ease of repair
• Weighted Shortest Job First (WSJF) method – priorities based on the quotient of business value and task size
• Risk-based approach – critical areas for security and stability first
• Opportunity cost model – comparing the cost of reducing debt with the cost of holding it

What tools help to ethically manage technology debt?

Today’s development teams have at their disposal an extensive ecosystem of tools that significantly support the process of identifying, monitoring and reducing technology debt. In the area of source code analysis, SonarQube and its cloud-based counterpart SonarCloud are particularly popular, offering comprehensive code quality analysis, detection of potential bugs and security vulnerabilities in real time. These tools automatically calculate the rate of technology debt expressed in “man-days” needed to reduce it, making it easier to communicate with business stakeholders. For JavaScript and CSS teams, ESLint and StyleLint are invaluable aids that enforce coding standards right from the code-writing stage, thereby preventing new debt.

Depending on the technologies used, teams reach for specialized tools like NDepend, which offers deep dependency and complexity analysis for .NET-based platforms, or JaCoCo, which provides precise measurement of code coverage with tests for Java ecosystems. Tools such as CodeClimate automate the process of assessing code quality by continuously analyzing changes made to the repository and alerting on potential quality issues before they can propagate into production code.

However, simply identifying tech debt is only the first step – for effective management, systems are needed to track and systematically reduce identified issues. Development teams often adapt tools like Jira or Azure DevOps, introducing dedicated ticket types specifically for elements of technical debt, allowing them to be tracked on par with regular development tasks. Platforms like Confluence or an organization’s internal Wiki are used to document key architectural decisions (ADRs – Architecture Decision Records), including the debt consciously incurred along with the business case and plans for future repayment. For teams preferring a more visual approach, tools such as Trello enable transparent management of technical debt backlog in the form of kanban boards.

Equally important are tools dedicated to visualizing and reporting on the status of technology debt. Solutions like Code Health offer clear dashboards displaying key code quality metrics, allowing you to track trends and quickly identify areas that need attention. Structure101 provides advanced capabilities to visualize the complexity of architecture and dependencies in code, helping to understand the structural aspects of technology debt. CodeScene, on the other hand, uses advanced algorithms to analyze the history of changes in the repository, identifying so-called “hotspots” – areas of code that are frequently modified, which are highly likely to be burdened with significant technical debt and are a potential source of future problems.

Minimum tool requirements for debt management

• Automated static analysis as part of CI/CD
• System for documenting and tracking identified debt
• Integration of quality metrics into the code review process
• Regular reporting of technical debt status to all stakeholders

Can automation and AI reduce the cost of maintaining technology debt?

The rapid development of artificial intelligence and advanced automation techniques is opening up entirely new perspectives in technology debt management, offering tools that are radically transforming the traditional approach to this challenge. Modern code analysis systems using machine learning techniques go far beyond the capabilities of conventional static analyzers. They can automatically identify subtle anomalies and problematic patterns in code that often remain invisible to traditional tools. What’s more, advanced AI algorithms are able to predict which areas of code carry the highest risk of failure, based on analysis of historical incidents and the characteristics of changes made to the system. Based on these analyses, AI-supported systems can suggest specific, contextually relevant refactorings, and in some cases even automatically implement simple fixes without human intervention, realizing the concept of auto-remediation.

In the area of software testing, artificial intelligence solutions are making revolutionary improvements that significantly increase the efficiency of problem detection and prevention. One of the most promising functionalities is the automatic generation of tests for low-coverage code fragments, which makes it possible to quickly eliminate one of the most common forms of technical debt. AI systems also excel at intelligently prioritizing test cases, analyzing error history and identifying those paths most likely to contain hidden defects. Particularly valuable is the ability of these systems to detect inconsistencies between documentation and actual implementation, and to predict potential side effects of changes made, significantly reducing the risk of functionality regression.

Groundbreaking tools such as GitHub Copilot and similar programming assistants based on large language models (LLMs) bring a new quality to the process of refactoring and managing legacy code. Their strength lies in their ability to assist developers in understanding complex, poorly documented code, which has traditionally been one of the biggest challenges in working with technically indebted systems. Not only do these tools suggest fixes in line with modern programming practices, but they can also automatically generate missing technical documentation and support migration processes to newer technologies. As a result, tasks that previously required weeks of tedious, manual work can be completed much faster and with less risk of introducing new bugs, significantly lowering the barrier to entry for technology debt reduction initiatives.

Practical applications of AI in technology debt management

• Proactive identification – detecting potential problems before they occur
• Automated repairs – implementation of simple fixes without human intervention
• Predictive analysis – predicting areas of concern
• Documentation support – automatic generation and updating of technical documentation

How do you build an organizational culture that consciously manages technology debt?

Building an effective culture of technology debt management requires a coordinated effort at all levels of an organization, from top management to development teams to day-to-day software development processes. Only a holistic approach can ensure a lasting change in the approach to technology debt that will stand the test of time and business pressures.

At the management level, it is crucial to strategically treat the issue of technical quality as equal to other business objectives. This means, first and foremost, incorporating metrics related to technology debt into the organization’s key performance indicators (KPIs), giving them similar weight as financial or operational indicators. Conscious organizations allocate dedicated resources to systematic debt reduction, adopting as standard the dedication of a minimum of 20% of team time to initiatives that improve the technical quality of systems. Equally important is publicly recognizing and rewarding activities that improve code and architecture quality, which signals to the entire organization that these aspects are indeed valuable and appreciated. The most effective leaders model the desired behavior by consistently making decisions that take into account long-term technical quality, even when that means compromising short-term business goals.

At the level of development teams, building a culture of conscious debt management begins with the implementation of the “leave the code better than you found it” principle as a fundamental standard for daily development work. This principle, derived from the Boy Scout movement (“leave the campground cleaner than you found it”), encourages every team member to make small improvements with every interaction with the code. Regular educational sessions on identifying and managing technical debt build a shared understanding of the problem and its consequences. Practices such as pair programming and systematic code reviews that focus not only on functionality but also on technical quality reinforce a culture of code care. It is also important to consciously celebrate successes in reducing technology debt as enthusiastically as implementations of new functionality, helping to break the traditional perception of code quality work as less valuable than product development.

At the manufacturing process level, conscious management of technology debt requires systematic integration of this aspect into the team’s daily practices. In agile methodologies, this means dedicating a portion of sprint capacity to debt reduction tasks, whether in the form of adjustable “refactoring days” or entire “quality weeks” dedicated solely to improving the technical health of systems. It is crucial to expand the standard definition of task completion (Definition of Done) to include quality criteria, such as test coverage, compliance with coding standards or absence of violations identified by static analysis tools. In modern organizations, automation of technology debt detection is becoming an integral part of DevOps practices, where quality metrics are monitored in real time, and exceeding set thresholds can automatically block the deployment of changes to production environments.

Elements of a culture of conscious management of technological debt

• The principle of “pay as you go” – Regular repayment of debt while working on functionalities
• Technical reading – regular code analysis sessions in the team
• Transparent reporting – open communication of technical status
• Shared responsibility – debt is not “pushed” onto individuals or teams

What ethical practices should accompany the decision to accept debt?

In engineering practice, complete elimination of technology debt is rarely possible or even desirable – instead, a conscious, ethical approach to the decision to accept it is crucial. Ethical incurring of technological debt is fundamentally different from negligence or ignorance – it is based on a conscious, transparent decision made based on a thorough understanding of the consequences. Every case of conscious acceptance of technical debt should go through a formalized decision-making process that ensures that the trade-off is truly justified and responsibly managed.

The first step in such a process is a sound analysis of the need for debt. The technical team, together with business stakeholders, should critically assess whether a quality trade-off is really necessary, or whether there are alternative methods to achieve the business goal without generating debt. Too often, organizations reach for “interim solutions” as a first choice, without a thorough consideration of other options. Another element is a detailed assessment of the consequences – an interdisciplinary team should analyze both the short- and long-term effects of a given decision, taking into account the impact on system stability, security, scalability and future maintenance and development costs. It is also important to precisely define the limits of acceptable debt – the team should define what level of compromise is acceptable in a given situation, and what conditions must be met for the debt to be repaid. Finally, a key element of an ethical approach is the creation of a concrete repayment plan, including clearly defined steps and deadlines for reducing the debt incurred.

The foundation of ethical practices in accepting technology debt is full transparency and accurate documentation. Any informed decision to incur debt should be recorded in the form of an Architecture Decision Record (ADR) or similar document that includes all relevant information. Such a record should include a detailed business context justifying the decision, a list of alternatives considered with reasons for their rejection, specific reasons for choosing a debt-generating solution, a thorough analysis of the associated risks and consequences, and a precise repayment plan and schedule. Such a document serves not only as an organization’s institutional memory, but also as an educational tool and a reference for future decisions.

Responsible organizations also establish clear, impassable ethical boundaries – defining the types of technology debt that are never acceptable, regardless of business or time pressures. Any compromise in the area of data security that could expose users to privacy risks certainly falls into this category. Similarly, solutions that knowingly violate regulatory compliance with industry or legal regulations are a line that an ethical organization should not cross. Known issues that negatively impact the accessibility of systems for people with disabilities are also on this list, as they lead to digital exclusion. It is particularly reprehensible to knowingly introduce errors or imperfections that are then hidden from customers, which is a violation of basic business integrity.

Framework for ethical acceptance of technological debt

• The principle of full awareness – all decision makers understand the technical and business implications
• Transparency principle – debt is visible, documented and tracked
• Proportionality principle – business benefits clearly outweigh future costs
• Principle of accountability – clearly defined responsibility for debt repayment

How do you strike a balance between rapid innovation and control of technology debt?

Achieving an optimal balance between rapid innovation and control of technology debt is one of the greatest challenges of today’s technology organizations. This balance is not a static point, but a dynamic process that must evolve with the product lifecycle and changing business priorities. A strategic approach to this challenge requires differentiating practices and acceptable trade-offs depending on the stage of product development.

In the experimental phase, involving the creation of an MVP (Minimum Viable Product) or proof of concept, it is reasonable to adopt a higher tolerance for technological debt. At this stage, the priority is to quickly validate business hypotheses and verify that the product has a real application in the market. Teams often consciously accept a higher level of technical debt, focusing on delivering key functionality with limited automated testing. However, it is important to plan already at this stage for a potential code rewrite strategy if the business concept is successful. This type of conscious planning significantly differentiates strategic debt incurrence from simple negligence.

When a product enters the growth and scaling phase, once its business value is confirmed, the priority becomes to gradually reduce the debt previously incurred. This is the moment when organizations should invest significantly in test automation and infrastructure, which allows for faster detection and remediation of problems. During this phase, it is also crucial to refactor critical elements of the architecture that may be bottlenecks for further development and scaling. In parallel, teams are implementing stricter quality standards and formalizing code review processes, preventing the creation of new debt during intensive functionality development.

At the product maturity stage, when the product has reached a stable market position, organizations can afford to introduce even more stringent code and architecture quality standards. At this stage, the focus shifts to preventive debt management – teams proactively look for potential problems and resolve them before they become critical. Systematic technological upgrades prevent obsolescence of the solutions used, while a conscious balancing of resources between maintaining existing functionality and developing new ones ensures the technical health of the system in the long term.

Regardless of the product development phase, modern technology organizations have developed a number of methods and practices that help balance innovation with technical quality. The implementation of feature flags (functionality switches) enables the gradual deployment and testing of new solutions, minimizing risk and enabling the rapid rollback of problematic changes. DevOps practices, with particular emphasis on automating manufacturing and deployment processes, ensure quality control while maintaining speed of delivery. Modular architecture, with clearly defined interfaces between components, allows independent evolution of individual parts of the system, reducing the spread of debt. Also gaining popularity is the platform approach, where central platform teams provide high-quality, reusable components that product teams can use to rapidly create new functionality without compromising on quality.

Practices that balance innovation and technical quality

• TDD/BDD techniques – ensuring quality from the start
• Dual-track agile model – separation of discovery and delivery paths
• Micro-refactoring – continuous small improvements as part of daily operations
• Strategic stabilization periods – dedicated cycles for debt reduction

How do you measure the impact of technology debt on a company’s business value?

Accurately measuring the impact of technology debt on an organization’s business value requires a holistic approach that combines technical indicators with business metrics to create a complete picture of the relationship between the technical health of systems and business performance. The key challenge is to translate abstract technical concepts into a language that business decision-makers can understand and that clearly illustrates the financial and strategic consequences of neglecting technical quality.

A fundamental group of indicators are software delivery performance metrics, derived from DevOps methodology and popularized by the State of DevOps Report survey. Lead time for changes, measured from the time an idea is conceived to its deployment to production, is an excellent barometer of an organization’s flexibility. In systems burdened by high technical debt, this time gradually increases, limiting a company’s ability to respond quickly to market changes. Deployment frequency directly correlates with the level of automation and quality of CI/CD processes, which in turn are more difficult to implement in systems characterized by high technical debt. A particularly telling indicator is the Mean Time To Recover (MTTR), which lengthens as debt increases, as well as the change failure rate, which shows what percentage of deployments cause production problems that require immediate intervention.

From a financial perspective, the impact of technology debt can be seen most clearly in an organization’s IT cost structure. The maintenance cost ratio shows what percentage of the IT budget is spent on maintaining existing systems versus developing new functionality. In organizations burdened by high debt, the ratio often exceeds 80:20 in favor of maintenance, drastically reducing the funds available for development initiatives. Equally important is the cost of change ratio, which measures the evolution of the effort required to implement similar functionality over time – a rising value of this ratio is a clear symptom of mounting debt. Some organizations also successfully use the ROI methodology of refactoring, measuring the specific savings from investments in debt reduction compared to the cost of maintaining it. The most comprehensive indicator is the TCO (Total Cost of Ownership) of IT systems, taking into account the full spectrum of costs associated with owning, maintaining and developing IT systems.

Complementing the financial perspective are operational indicators that show how technology debt affects the day-to-day operations of the organization. Engineering Velocity illustrates the rate at which technical teams deliver business value, which naturally decreases as technical debt increases. Analysis of Code Churn, or the frequency of changes to the same pieces of code, identifies problem areas that require continuous fixes. An indirect but telling indicator is the level of turnover in IT teams and the associated costs of recruiting and onboarding new employees – high technology debt often leads to developer frustration and departures. Finally, the number of production incidents and their impact on SLA levels or system availability provide tangible evidence of how technical neglect translates into business problems.

From the perspective of the end user, technology debt manifests itself through a number of user experience (UX) metrics that directly affect a product’s business success. The loading time and overall responsiveness of an application deteriorate as the debt builds up, which translates into user satisfaction. The number of errors encountered while using a system is a direct indicator of its technical quality. These technical aspects then translate into key business metrics – conversion and customer retention rates, which often deteriorate as technical problems mount. Ultimately, metrics such as Net Promoter Score (NPS) or overall customer satisfaction provide a synthetic measure of how all these factors affect users’ perceptions of the product.

Framework for assessing the business impact of technology debt

• Opportunity cost – delayed introduction of functionality to the market
• Operating cost – additional resources needed to maintain unstable systems
• Reputational cost – loss of customer confidence due to quality problems
• Cost of human capital – reduced talent retention and recruitment difficulties

How to communicate technology debt risks to non-technical stakeholders?

Effective communication with non-technical stakeholders requires translating complex technical concepts into the language of business consequences and risks. Instead of using specialized jargon, focus on:

Concrete business consequences:

• “Every change in the payment module now takes 3 weeks instead of 3 days.”
• “The risk of failure during peak sales increased by 35%.”
• “It will require 40% more time to implement integration with a new partner.”
• “The cost of maintaining the system is increasing by 15% per year due to increasing complexity.”

Effective visualizations:

• Trend charts showing the decline in team productivity over time
• Heat maps showing the concentration of problems in critical areas of the system
• Comparisons of delivery times for similar functionality in different parts of the system
• Financial simulations showing the long-term consequences of ignoring the problem

Analogies understood by decision makers:

• Comparison to building maintenance – postponed repairs lead to more expensive repairs
• Analogy to financial debt – interest on technology debt is growing exponentially
• Metaphor for chronic disease – untreated leads to more serious complications
• Comparison to fleet management – regular maintenance vs. breakdowns

Engagement strategies:

• Workshop simulations showing the impact of debt on the ability to respond to market changes
• Reports from the customer’s perspective – how debt affects the user experience
• Post-debt reduction success stories – concrete, measurable business improvements
• Industry benchmarks showing competitive technical quality management practices

Effective communication strategies with technological debt

• The principle of “so what?” – every technical information must end with a business consequence
• Three levels of communication – strategic (board), tactical (managers) and operational (teams)
• Technical debt scorecard – regular report to management with key metrics
• Storytelling – concrete examples showing the impact of debt on daily operations

Are there universal standards for documenting and tracking technology debt?

While there is no single global standard, effective practices for documenting and tracking technology debt have evolved and can be tailored to an organization’s needs.

Structured documentation methods:

1. Architecture Decision Records (ADRs) – documents that record key technical decisions, including knowingly incurred debt:
   • Decision context (business and technical)
   • Alternatives considered
   • Consequences and accepted limitations
   • Plan and deadlines for revision/debt repayment
2. TODO/FIXME technique with extensions:
   • Standard designations in the code (TODO, FIXME, DEBT)
   • Expanded to include: priority, deadline, owner, estimated cost of repair
   • Automatic tracking and reporting by CI/CD tools
   • Integration with task management systems (e.g. automatic task creation)
3. Technology debt catalog:
   • Central register of all known debt elements
   • Classification by type, impact, priority and cost of repair
   • Link to system components and business objectives
   • Regular reviews and status updates

Tracking and monitoring systems:

1. Integration with project management tools:
   • Dedicated notification/task types for technical debt
   • Special designations for refactoring stories
   • Categorization to enable filtering and reporting
   • Link to epics and business initiatives
2. Dashboards and reporting:
   • Dedicated dashboards showing debt status and trends
   • Regular reporting to technical and business stakeholders
   • Metrics showing the impact of debt on teams’ KPIs
   • Visualizations depicting debt “hot-spots” in the system

Elements of an effective debt documentation system

• Classification of debt types – architectural, code, test, documentation, infrastructure
• Standard format – a consistent debt documentation template throughout the organization
• Life cycle – clearly defined states from identification to closure
• Link to the development process – integration with existing DevOps tools and practices

What are the long-term consequences of ignoring the ethical dimension of technological debt?

Systematic disregard for the ethical dimension of technology debt has profound, multidimensional consequences, the effects of which go far beyond purely technical problems with code or architecture. Over time, these omissions permeate all aspects of an organization’s operations, fundamentally affecting its culture, market position and broader social context.

On the intra-organizational level, the first and most serious consequence is the gradual erosion of the engineering culture. When an organization consistently tolerates a growing technological debt, a progressive acceptance of low standards and “makeshift” solutions develops among technical teams. What was initially the exception gradually becomes the norm, leading to a decline in the overall quality of work. This phenomenon is often referred to as the “broken glass syndrome” – just as one broken element in a neglected building encourages further vandalism, so the first unrepaired technical problems provoke the introduction of more workarounds and imperfect solutions, eventually leading to a complete breakdown of engineering discipline.

Another acute effect is the loss of the most valuable talent. The best professionals, with the most options on the job market, are usually the first to leave organizations that disregard technical quality. The frustration of constantly grappling with the consequences of historical neglect, without the opportunity to make meaningful improvements, leads to professional burnout and the search for more rewarding opportunities elsewhere. This, in turn, leads to a deepening crisis of technical leadership – the authority of leaders who tolerate or promote short-sighted quality compromises is systematically undermined, further weakening their ability to bring about positive change. In the long run, the organization experiences increasing tension and conflict between product teams striving to deliver new functionality quickly and development teams struggling with the consequences of previous compromises.

From a market perspective, the long-term consequences are equally serious. Organizations burdened by high technological debt experience a systematic decline in competitiveness, resulting from extended response times to market changes and competitive actions. Repeated quality problems and failures lead to a gradual loss of confidence among customers, who eventually seek more reliable alternatives. At the same time, rising operating costs associated with maintaining unstable, difficult-to-modify systems make the company uncompetitive on price. This combination of factors leads to difficulties in attracting new investment – potential investors quickly identify an unsustainable product development model as a significant risk. Ultimately, the organization becomes much more susceptible to disruption from more agile competitors who, with a cleaner code base, can adapt more quickly to changing market needs.

From a broader social and ethical perspective, the consistent ignoring of technological debt contributes to the creation of a “toxic legacy” – future generations of engineers will be forced to grapple with the consequences of today’s neglect, wasting their potential to solve problems that could have been avoided. From a societal perspective, this represents a waste of valuable resources – energy, time and talent that could be used to create new value and solve important problems. Particularly worrisome is the increased risk of serious security or privacy incidents that can directly affect the safety and well-being of users. Industry-wide, this approach contributes to the perpetuation of inefficient practices when problems are systematically hidden instead of solved, leading to a general lowering of professional standards and the erosion of an engineering ethos based on responsibility and the pursuit of excellence.

Symptoms of an organization ignoring the ethical dimension of technological debt

• Cyclical “rewrite of the system from scratch” every few years
• Chronic failure to adhere to project schedules
• High turnover in technical teams (more than 25% per year)
• Growing communication gap between business and IT
• Recurring problems with implementations and regression of functionality

How to prepare a technology roadmap that takes into account systematic debt reduction?

Developing an effective technology roadmap that harmoniously integrates systematic technical debt reduction with product development requires a thoughtful, strategic approach that combines business and technical goals. Such a roadmap cannot be an isolated initiative of the IT department, but must be an integral part of the broader product and organizational development strategy. The process of creating it involves several key steps that together form the foundation of a sustainable roadmap.

The starting point is always a comprehensive inventory of existing technical debt across all of an organization’s systems. This step requires an in-depth technical audit that goes beyond a superficial look at the code and covers all technology layers – from source code, to architecture, to testing, to infrastructure, to documentation and processes. Identified debt elements should be categorized by type (e.g., architectural debt, code debt, test debt) and functional areas of the system. It is particularly important to estimate the “interest cost” of each debt element – the additional workload that the team must incur because of their existence. This comprehensive map of technical debt forms the foundation of the entire planning process and enables informed decisions on prioritizing activities.

The second key step is precisely prioritization based on objective data and analysis. This process must take into account the complex interrelationships between different elements of debt – some problems must be addressed sequentially, while others can be addressed in parallel. Equally important is a careful assessment of the business impact of the various areas of debt – which ones most constrain product development, generate the highest maintenance costs or pose the greatest business risk. A good practice is to identify so-called “quick wins” – relatively easy-to-implement improvements that produce a significant positive impact with relatively little effort. The final element of this step is to determine a critical path for debt reduction, identifying a sequence of actions that minimizes risk and maximizes business value.

The third, and often most difficult, aspect is the integration of the debt reduction plan into the normal product development cycle. Unlike the traditional approach, where debt reduction is treated as a separate, one-time initiative, an effective strategy requires a systematic, continuous effort integrated into the regular manufacturing process. In practice, this means allocating dedicated time in each sprint (typically a minimum of 20% of the team’s capacity) to debt reduction tasks, and integrating these tasks into the normal product backlog, where they are subject to the same planning and tracking processes as business functionality. Larger refactoring initiatives should be planned in parallel with functionality development, and ideally debt reduction should be synchronized with the planned development of related areas of the product – the team naturally streamlines those parts of the system where it plans to introduce new functionality.

The fourth step involves identifying the resources and tools needed to implement the plan. This includes identifying the specific competencies needed to address specific types of debt – some issues may require specialized knowledge that the team lacks. Equally important is the selection or development of appropriate tools to support debt monitoring and reduction, from static analyzers to CI/CD systems to tools for tracking quality metrics. This step also includes identifying the team’s training needs – investment in improving competencies can significantly increase the effectiveness of reduction efforts. All of this must be supported by appropriate budgeting for technical quality improvement initiatives, which requires a compelling business case.

The final element is defining precise metrics of success and controls to monitor progress and adapt the plan to changing circumstances. It is crucial to establish measurable indicators of progress that will be regularly tracked and reported. Equally important is scheduling regular reviews and checkpoints during which the team can assess the effectiveness of activities and make necessary adjustments. Different stakeholder groups – from development teams to senior management – require dedicated reporting mechanisms tailored to their perspective and information needs. Finally, a formal process for revising and adapting the plan based on changing business priorities and new technical discoveries is essential. Flexibility and adaptability are critical success factors in long-term technology debt management.

Structure of an effective technology debt reduction roadmap

• Short-term horizon (0-3 months) – “quick wins” and critical security issues
• Medium-term horizon (3-12 months) – strategic refactoring projects
• Long-term horizon (1-3 years) – architectural transformation and modernization
• Continuous action – practices to prevent the creation of new debt
• Milestones – clearly defined points to verify progress

Can outsourcing software development exacerbate or minimize the problem of technology debt?

Outsourcing software development introduces an additional layer of complexity to the already complicated issue of managing technology debt. Its impact on the technical health of systems can be both positive and negative, depending on the cooperation model adopted, the contract structure and the organizational culture of both parties. Understanding these dynamics is crucial for organizations that choose to outsource some or all of their software development to third-party vendors.

Traditional outsourcing models often add to the problem of technology debt. This is particularly evident in contracts based on the Fixed Price billing model, where the supplier agrees to deliver a certain amount of functionality for a predetermined amount. Such a structure favors speed of delivery over technical quality, as any additional code or architecture enhancement not explicitly stated in the specification directly reduces the supplier’s margin. As a result, third-party developers naturally seek to minimize their own costs at the expense of internal system quality, focusing only on meeting the client’s functional requirements to the smallest extent possible. Such a model lacks the natural incentives to invest in long-term technical quality, scalability or maintainability of the code, which becomes “the customer’s problem” once the project is completed.

Another factor exacerbating technological debt in the context of outsourcing is the fragmentation of responsibility. In the classic model, there is a clear separation between development teams (often external) and maintenance teams (usually internal), leading to the “flip over the wall” syndrome. – Developers deliver code without suffering the consequences of its quality in the production phase. During project handover, there is a significant loss of contextual and historical knowledge, which significantly hinders subsequent maintenance and development of the system. There is also a phenomenon of “shifting responsibility” between the customer and supplier teams – each party tends to blame the other for quality problems, leading to a stalemate in which no one feels real responsibility for solving the growing problems.

Information asymmetry between the parties to the cooperation is also a significant challenge. Customers often do not have the technical competence to properly assess the quality of delivered solutions – they focus on whether the system meets functional requirements, overlooking issues of technical debt hidden “under the hood.” Third-party vendors, aware of this asymmetry, may be inclined to hide technical problems that do not manifest themselves with immediately visible symptoms. The lack of transparent communication about the status of technical debt and its potential consequences further exacerbates the problem, leading to the unconscious accumulation of risks that will only manifest themselves in the future.

However, there are outsourcing models that, when properly implemented, can make a significant contribution to minimizing technology debt. Particularly promising are value-based collaboration models such as Team Leasing or Staff Augmentation, where the client acquires a dedicated team of external specialists working in its environment, according to its standards and processes. Transparent billing of time instead of a fixed price per scope allows for proper balancing of work on functionalities and technical quality. In such a model, it is possible to truly share responsibility for quality between the client and the supplier, especially when the contract is viewed as a long-term strategic partnership rather than a one-time deal.

A key success factor is the deep integration of external teams into the client organization. This includes adopting common quality standards, development methodologies and processes, and tools to ensure technical and cultural consistency. Particularly valuable is the creation of mixed teams made up of both customer and vendor employees, which promotes knowledge transfer and builds a shared sense of responsibility. In an ideal scenario, third-party developers take on shared responsibility for system maintenance, which naturally motivates them to care about the technical quality of the delivered solutions.

Some organizations are successfully taking a strategic approach to outsourcing, focusing on acquiring specific competencies not available internally. Instead of outsourcing all development, they selectively acquire experts with experience in specific areas, such as refactoring legacy systems or architectural modernization. This fine-tuning of competencies allows targeted addressing of the most serious areas of technical debt while building internal know-how. Teams are formed around specific technical challenges, with a clearly defined goal and expected results, which promotes efficiency and quality of delivered solutions.

Regardless of the collaboration model adopted, appropriate quality control mechanisms are crucial. Regular, independent code and architecture audits by trusted third parties help objectively assess the technical health of a system and identify potential problems early. Incorporating common quality metrics into SLAs (Service Level Agreements) formalizes expectations for technical standards and introduces measurable success criteria beyond functionality. Automating the verification of quality standards through integrated CI/CD tools provides objective, systematic checks without the need for manual reviews, dramatically increasing the efficiency of the process and reducing the risks arising from the human tendency to skip or simplify check steps.

Collaboration model to minimize technology debt risk

• Transparency – full customer access to code, quality metrics and documentation
• Common standards – agreed development practices followed by all parties
• Long-term perspective – taking into account TCO (Total Cost of Ownership) and not just the initial cost
• Continuous verification – regular code reviews and quality audits
• Shared responsibility – a compensation model that takes into account technical quality

How do regulations (e.g., the NIS2 directive) affect technology debt management?

The evolution of the regulatory environment, with the Network and Information Systems (NIS2) directive at the forefront, is fundamentally changing the context of technology debt management, moving it from a purely technical dimension to the realm of legal compliance and formal organizational risk management. Unlike its predecessor, NIS2 significantly expands the scope of the regulation’s subject matter to include a wider range of organizations deemed “essential” or “key” to the functioning of the economy and society, including companies in the energy, transportation, banking, digital infrastructure, healthcare or government sectors. For these entities, technology debt management ceases to be an internal matter for IT teams, and becomes part of a mandatory cyber security management system, subject to external audits and potential sanctions.

The most significant NIS2 requirement affecting technology debt management is the imposition on organizations to implement a systematic, documented approach to cyber security risk management. In practice, this means identifying, analyzing and mitigating risks arising from technical deficiencies in systems, including those caused by accumulated technology debt. Organizations must now formally document the technical status of their systems, keep records of identified problems and implement plans to systematically address them. Of particular importance is the requirement for regular security audits and penetration tests, which often reveal problems directly related to technical debt – outdated libraries, unpatched vulnerabilities or suboptimal security configurations.

A revolutionary aspect of the NIS2 directive is the introduction of direct responsibility of board members for ensuring adequate cyber security measures. In the case of serious incidents resulting from negligence, executives can be held personally liable, including financially. This provision fundamentally changes the intra-organizational dynamics around technology debt management – topics previously considered too technical for the board level are becoming the subject of regular reports and strategic decisions. In many organizations, this is leading to a much more serious consideration of investments in technical debt reduction, particularly in areas related to security and systems resilience.

The incident reporting requirement is another aspect that directly affects technology debt management. Under the NIS2 directive, organizations are required to report major security incidents to the appropriate supervisory authorities within 24 hours of detection, and then provide a detailed report within 72 hours. The transparency enforced by this requirement means that organizations can no longer hide the technical problems that led to an incident. As a result, there is a growing incentive to proactively address technology debt before it leads to a situation requiring public reporting.

A particularly interesting aspect of NIS2 is the extension of responsibility to the entire IT supply chain. Organizations must now assess the risks associated with third-party providers of IT services and components, which directly affects outsourcing practices. Technology debt in externally provided systems becomes a formal risk to the organization that must be managed with the same care as internal systems. In practice, this leads to the inclusion of technology debt management requirements in vendor contracts, detailed audits of the code and architecture of delivered solutions, and increased oversight of the technical quality of outsourced projects.

The regulatory dimension of technical debt management is not limited to the NIS2 directive. Other legislation, such as RODO in the context of personal data protection, industry-specific regulations for the financial sector (e.g., DORA – Digital Operational Resilience Act in the EU), or sector-specific security standards (e.g., HIPAA for the healthcare sector in the US) also introduce similar requirements. This regulatory convergence is creating a complex landscape of compliance requirements that increasingly address issues of technology debt, although often without explicitly calling it by that term.

In practice, organizations are adapting to the new regulatory requirements by formalizing their technology debt management processes. This includes the introduction of mandatory, regular technical audits, the creation and maintenance of a log of identified issues with assigned risk levels, and a formal prioritization and remediation process. Some organizations are implementing dedicated technology debt committees that report regularly to the board to ensure adequate oversight of this area. The “compliance as code” approach is also becoming increasingly popular. – automating verification of compliance with regulatory requirements through code and configuration analysis tools integrated into the manufacturing process.

Paradoxically, increasing regulatory requirements, while placing an additional burden on organizations, can simultaneously act as a catalyst for positive change in the area of technology debt management. Formal compliance requirements provide tech leaders with a compelling business case for investments in debt reduction that may have previously been difficult to justify. The threat of potential financial penalties and reputational consequences appeals to business decision makers more directly than technical arguments. As a result, organizations that previously downplayed the problem of technology debt are now motivated by external requirements to take systematic corrective action.

A practical framework for NIS2 compliance in the context of technology debt

• Inventory of critical systems – identification of key regulated assets
• Risk classification of debt – assessing the impact on security and compliance
• High-priority recovery plan – eliminating critical debt from a compliance perspective
• Cyclical evaluation process – regular reviews of technical compliance
• Reporting mechanisms – transparent reporting of compliance status to stakeholders

How does ethical debt management strengthen a company’s competitiveness in the market?

Ethical management of technological debt goes far beyond the moral and technical dimensions, translating into concrete, measurable competitive advantages that fundamentally affect a company’s position in a dynamic market ecosystem. Organizations that systematically and responsibly address the technical quality of their systems gain multidimensional business advantages that, over time, become strategic differentiators from competitors focused solely on short-term goals.

Operationally, companies with low levels of technology debt enjoy unparalleled flexibility and the ability to respond quickly to changing market conditions. Research conducted by DevOps Research and Assessment (DORA) consistently shows that organizations with high technical maturity can deploy new functionality up to 2-3 times faster than their competitors struggling with technical debt. This speed of translating ideas into tangible solutions available to customers is a fundamental advantage in the digital age, where time-to-market often determines the success or failure of business initiatives. Equally important is responsiveness to changing customer needs – companies with technically “clean” systems can adapt their products to new requirements much faster, thus maintaining customer loyalty and capturing new market segments.

A significant operational advantage is also the increased stability and reliability of systems that are not burdened by accumulated technical debt. Such organizations experience significantly fewer production incidents, and those that do occur are typically diagnosed and repaired more quickly. This translates directly into a better user experience, reduced costs associated with downtime and emergency interventions, and a stronger brand reputation for reliable solutions. In the long run, it also leads to significantly lower system maintenance costs – some studies indicate savings of 30-40% compared to organizations struggling with high technical debt, freeing up financial resources for development and innovation initiatives.

In the area of talent management, an ethical approach to technology debt brings equally tangible benefits. Top IT professionals strongly prefer to work for organizations that care about code quality and give them space for professional development based on best engineering practices. Companies known for their high technical culture and responsible approach to technology debt become natural “talent magnets,” attracting the best specialists without having to compete solely on salary. At the same time, such organizations enjoy significantly lower turnover of technical teams – studies show that companies that effectively manage technical debt can achieve up to 25% lower departure rates than their competitors who ignore this aspect.

The best development teams operating in an environment with low levels of technical debt also achieve significantly higher productivity. Developers spend more time creating new value for customers and less time “putting out fires” and grappling with the consequences of historical neglect. This translates into higher team morale, greater commitment and innovation. In addition, such organizations experience much better collaboration between technical and business teams, as the absence of constant technical crises allows them to build partnerships based on mutual trust and a long-term perspective, instead of conflicts arising from tensions between business needs and technical constraints.

From a purely business perspective, ethical management of technology debt translates into a number of strategic advantages. Such organizations enjoy much better adaptability – the ability to quickly transform their business model in response to changing market dynamics, new regulations or unexpected crises. This strategic flexibility is becoming a key factor for survival and success in the unpredictable, changing business environment of the 21st century. From a financial point of view, IT systems with low technical debt are more valuable assets for an organization, which translates into a higher valuation for the company in investment deals or acquisitions.

Investors are increasingly considering the technical health of IT systems as an important factor in assessing investment risk – organizations with a systematic approach to managing technology debt are seen as less risky and more forward-looking investments. They also gain a significant advantage in building long-term relationships with customers – the reliability of systems, responsiveness to needs and ability to constantly innovate build trust, which translates into loyalty and positive recommendations. In the subscription economy, where customer retention is a key profitability factor, this advantage is fundamental to long-term success.

Ethical management of technology debt also leads to optimized allocation of resources within the organization. Instead of continually investing in “firefighting” and fixing problems resulting from historical neglect, a company can focus its financial, time and human resources on initiatives with high business value – research and development of new products, expansion into new markets or deepening customer relationships. This ability to prioritize strategic initiatives rather than reactive crisis management is a fundamental competitive advantage in the long term.

Finally, organizations with low technology debt make better, more informed strategic decisions. Their information systems provide reliable, timely data that informs decision-making processes. Technical flexibility allows them to quickly test business hypotheses and iteratively refine strategies based on real market data. Unlike competitors struggling with outdated, inflexible systems, these organizations can undertake complex transformation initiatives with greater confidence of success and lower risk of failure.

Measurable indicators of competitive advantage

• Time-to-market – reduce time to introduce new functionality by 30-50%
• Customer acquisition cost – reduction due to more stable systems and better user experience
• Customer Lifetime Value – increase due to higher retention resulting from service reliability
• Operating margins – increase due to lower maintenance and incident handling costs
• Innovation indicators – number of new products and functionalities introduced annually

What technology trends will shape the approach to technology debt in the next decade?

The future of technology debt management will be shaped by the dynamic convergence of several fundamental technological and methodological trends that are radically transforming the way information systems are designed, produced and maintained. These overlapping trends are creating a new paradigm in which traditional approaches to technical quality management are giving way to more integrated, proactive and automated methods.

One of the most significant trends already revolutionizing approaches to technology debt is the evolution of architectural concepts. Traditional monolithic systems that required complex, risky refactoring are giving way to microservices architectures and the serverless paradigm, which enable incremental, incremental modernization. In such a model, instead of an overall system overhaul, organizations can systematically carve out, rewrite and replace individual components, minimizing risk and spreading costs over time. At the same time, the growing popularity of Platform Engineering introduces a new quality – in-house development platforms provide teams with a set of tools, components and best practices with built-in quality assurance mechanisms, naturally preventing new debt.

API-first design (API-oriented architecture) also significantly impacts technical debt management by promoting clearly defined contracts between components and allowing them to evolve independently. With this approach, organizations can upgrade individual parts of the system without affecting the whole, greatly facilitating systematic debt reduction. The growing importance of the Infrastructure as Code (IaC) approach brings good code management practices to the infrastructure domain, eliminating traditional infrastructure debt by automating, versioning and testing infrastructure configurations analogously to application code.

The second fundamental trend that will radically change the approach to technology debt is the explosive growth of tools using artificial intelligence and automation. Advanced AI-driven code quality systems are already capable of analyzing source code on an unprecedented scale, identifying subtle patterns that indicate potential quality problems and suggesting specific improvements. In the coming decade, autonomous remediation systems are expected to be developed that can automatically fix identified low-risk problems without human intervention, dramatically increasing the efficiency of debt management.

A particularly promising direction is predictive analytics for technical debt – advanced machine learning algorithms, by analyzing historical data on system development and maintenance, will be able to predict which areas of code are most likely to become problematic in the future, enabling proactive preventive action. The use of generative artificial intelligence in refactoring is also a breakthrough – models such as GPT-4 and GitHub Copilot are already assisting developers in transforming legacy code, and their capabilities will steadily increase, making technical debt reduction much more effective.

In parallel, we are seeing an evolution of software development paradigms that significantly affect technical debt management. DevSecOps represents a holistic approach, integrating security aspects throughout the entire development cycle, which prevents specific types of debt related to vulnerabilities and security gaps. The Value Stream Management methodology focuses on optimizing the flow of value through IT systems, identifying and eliminating bottlenecks resulting from technical debt that slow down the delivery of value to end users.

The growing importance of software supply chain security (Software Supply Chain Security) introduces a new layer of debt management – organizations need to systematically monitor and update external dependencies to minimize the risk from outdated or vulnerable libraries. At the same time, there is a visible trend of integrating the business into the software development cycle (BizDevOps), where business representatives are directly involved in technical processes, leading to a better understanding and informed acceptance of certain quality trade-offs in the context of business priorities.

The area of measuring and reporting technology debt is also undergoing significant changes. The industry is moving toward standardization of quality and debt metrics, creating common, standardized indicators that allow objective comparison of the technical condition of different systems. Increasing attention is being paid to total cost of ownership (TCO), which takes into account not only direct manufacturing costs, but also the long-term consequences of quality decisions. In the context of growing environmental awareness, a new dimension of evaluation is also emerging – Green Software Engineering, which takes into account energy efficiency and carbon footprint as important aspects of technical quality of systems.

An important trend is the growing importance of transparency in the context of software components. The concept of Software Bill of Materials (SBOM) – a detailed inventory of all components and dependencies used in a system – is becoming standard, enabling full transparency about potential technical debt arising from external libraries and frameworks. This trend contributes to more conscious risk management of external dependencies, which are often a hidden source of technical debt.

Culturally and organizationally, we are also seeing significant transformations affecting technology debt management. The concept of product teams being responsible for the entire product lifecycle, expressed by the slogan “you build it, you run it,” is becoming an industry standard. Teams that develop software are also responsible for maintaining it in the production environment, which naturally motivates attention to technical quality and debt minimization. At the same time, a Sociotechnical Engineering approach is developing that designs systems with the human aspects of development and maintenance in mind, creating environments that foster high technical quality.

Organizations are increasingly adopting a learning culture (Learning Culture), where incidents and problems resulting from technical debt are not treated as failures, but as opportunities for learning and systemic improvement. There is also a visible trend of adapting open source practices inside the organization (Inner Source), where internal components and libraries are developed in a model inspired by open source projects, which promotes higher quality and greater transparency in managing potential technical debt.

The future of technology debt management will therefore be significantly different from traditional approaches. It will be characterized by a shift of responsibility for quality to earlier stages of the manufacturing process (Shift Left Quality), where problems are identified and resolved before they become part of the production system. Artificial intelligence will play the role of a programmer’s partner, automating routine code quality maintenance tasks and allowing the human engineer to focus on more creative, strategic aspects. Instead of periodic, revolutionary refactorings, continuous, evolutionary architecture evaluation and improvement (Continuous Architecture Evaluation) will become the norm.

Regulatory compliance challenges will increasingly be addressed by a “Compliance as Code” approach, automating the verification and documentation of regulatory compliance. Finally, there will be an increasing focus on Developer Experience, optimizing developer tools, processes and environments in a way that promotes natural attention to technical quality and systematic prevention of new debt.

Key trends affecting technology debt management

• Developer experience (DX) – optimizing developers’ work environment for better quality
• Shift left quality – transfer of responsibility for quality to earlier stages of development
• AI as a programmer’s partner – automating routine code maintenance tasks
• Continuous architecture evaluation – continuous evaluation and evolution of architecture instead of revolution
• Compliance as code – automating compliance with regulatory requirements