Dealing with Technical Debt in Agile Development Projects

Companies are adapting their traditional development processes, aiming for project-specific designs that are referred to as “Agile Product Development” - flexible, adaptive and accelerated processes. Implementing these principles supports developers to react to challenges such as shortened innovation cycles. For successful implementation and use of these new principles, support functions such as procurement have to be adapted to the demands of flexible, agile and accelerated processes. Complexity of tasks and impact on corporate success of procurement increased significantly in the last decades resulting in specialized stand-alone procurement departments. Being optimized for traditional development methods such as stage-gate, these departments will be challenged by agile developers demanding for highly flexible, hardly predictable procurement activities. Therefore, the authors present a scientifically derived catalog of requirements for procurement in agile product and technology development projects, e.g. shortened lead times and vague technical specification of products to be procured. Based on this catalog, options for the configuration of procurement in agile development projects, such as the organizational merging of development and procurement, are discussed. Adapting the procurement according to the requirements derived in this paper will help companies to successfully conduct procurement activities in regard to the needs of agile new product development.

Technical debt (TD) happens when project teams carry out technical decisions in favor of a short-term goal(s) in their projects, whether deliberately or unknowingly. TD must be properly managed to guarantee that its negative implications do not outweigh its advantages. A lot of research has been conducted to show that TD has evolved into a common problem with considerable financial burden. Test technical debt is the technical debt aspect of testing (or test debt). Test debt is a relatively new concept that has piqued the curiosity of the software industry in recent years. In this article, we assume that the organization selects the testing artifacts at the start of every sprint. Implementing the latest features in consideration of expected business value and repaying technical debt are among candidate tasks in terms of the testing process (test cases increments). To gain the maximum benefit for the organization in terms of software testing optimization, there is a need to select the artifacts (i.e., test cases) with maximum feature coverage within the available resources. The management of testing optimization for large projects is complicated and can also be treated as a multi-objective problem that entails a trade-off between the agile software's short-term and long-term value. In this article, we implement a multi-objective indicator-based evolutionary algorithm (IBEA) for fixing such optimization issues. The capability of the algorithm is evidenced by adding it to a real case study of a university registration process.

Cross-disciplinary and cross-life-cycle-phase Technical Debt in automated Production Systems: two industrial case studies and a survey

In the fast-paced and highly competitive landscape of retail, the management of technical debt has become a critical challenge for organizations striving to maintain robust and scalable technology systems. Technical debt, the accumulated cost of prioritizing quick fixes or suboptimal solutions over more sustainable and efficient options, can significantly hinder an organization's ability to innovate, adapt, and grow. This paper explores the role of strategic leadership in effectively reducing technical debt within retail technology systems. By examining key leadership strategies, such as fostering a culture of continuous improvement, investing in long-term technology planning, and emphasizing cross-functional collaboration, the paper highlights how retail organizations can systematically address and mitigate technical debt. Strategic leadership in the retail sector involves a comprehensive understanding of both business and technological imperatives. Leaders must recognize the implications of technical debt not only in terms of immediate financial costs but also in the broader context of business agility, customer experience, and competitive advantage. The paper emphasizes the need for leaders to adopt a proactive approach, prioritizing technical debt reduction as a strategic objective that aligns with the organization's overall goals. One of the central arguments of the paper is that effective technical debt management requires a shift from reactive to proactive leadership. This involves anticipating potential issues, encouraging innovation, and making informed decisions that balance short-term gains with long-term sustainability. The paper discusses various leadership practices that contribute to this shift, including the implementation of robust governance frameworks, the adoption of agile methodologies, and the promotion of a learning-oriented organizational culture.

Technical debt refers to decisions made during the design and development of software that postpone the resolution of technical problems or the enhancement of the software’s features to a later date. If not properly managed, technical debt can put long-term software quality and maintainability at risk. Self-admitted technical debt is defined as the addition of specific comments to source code as a result of conscious and deliberate decisions to accumulate technical debt. In this paper, we will look at the presence of self-admitted technical debt in open-source blockchain projects, which are characterized by the use of a relatively novel technology and the need to generate trust. The self-admitted technical debt was analyzed using NLP techniques for the classification of comments extracted from the source code of ten projects chosen based on capitalization and popularity. The analysis of self-admitted technical debt in blockchain projects was compared with the results of previous non-blockchain open-source project analyses. The findings show that self-admitted design technical debt outnumbers requirement technical debt in blockchain projects. The analysis discovered that some projects had a low percentage of self-admitted technical debt in the comments but a high percentage of source code files with debt. In addition, self-admitted technical debt is on average more prevalent in blockchain projects and more equally distributed than in reference Java projects.If not managed, the relatively high presence of detected technical debt in blockchain projects could represent a threat to the needed trust between the blockchain system and the users. Blockchain projects development teams could benefit from self-admitted technical debt detection for targeted technical debt management.

JavaScript (JS) is one of the most popular programming languages for developing client-side applications mainly due to allowing the adoption of different programming styles, not having strict syntax rules, and supporting a plethora of frameworks. The flexibility that the language provides may accelerate the development of application, but also pose threats to the quality of the final software product, e.g., introducing Technical Debt (TD). TD reflects the additional cost of software maintenance activities to implement new features, occurring due to poorly developed solutions. Being able to forecast the levels of TD in the future can be extremely valuable in managing TD, since it can contribute to informed decision making when designating future repayments and refactoring budget among a company's projects. Despite the popularity of JS and the undoubtful benefits of accurate TD forecasting, in the literature, there is available only a limited number of tools and methodologies that are able to: (a) forecast TD during software evolution, (b) provide a ground-truth TD quantifications to train forecasting, since TD tools that are available are based on different rulesets and none is recognized as a state-of-the-art solution, (c) take into consideration the language-specific characteristics of JS. As a main contribution for this study, we propose a methodology (along with a supporting tool) that supports the aforementioned goals based on the Backward Stepwise Regression and Auto-Regressive Integrated Moving Average (ARIMA). We evaluate the proposed approach through a case study on 19,636 releases of 105 open-source applications. The results point out that: (a) the proposed model can lead to an accurate prediction of TD, and (b) the Number of appearances of the “new” and “eval” keyword along with the number of “anonymous” and “arrow” functions are among the features of JavaScript language that are related to high levels of TD.

Technical debt is the consequence of trade-offs made during software development to ensure speedy releases. The research community lacks rigorously evaluated guidelines to help practitioners characterize, manage and prioritize debt. This paper describes a study conducted with an industrial partner during their implementation of Agile development practices for a large software development division within the company. The report contains our initial findings based on ethnographic observations and semi-structured interviews. The goal is to identify the best practices regarding managing technical debt so that the researchers and the practitioners can further evaluate these practices to extend their knowledge of the technical debt metaphor. We determined that the developers considered their own taxonomy of technical debt based on the type of work they were assigned and their personal understanding of the term. Despite management's high-level categories, the developers mostly considered design debt, testing debt and defect debt. In addition to developers having their own taxonomy, assigning dedicated teams for technical debt reduction and allowing other teams about 20% of time per sprint for debt reduction are good initiatives towards lowering technical debt. While technical debt has become a well-regarded concept in the Agile community, further empirical evaluation is needed to assess how to properly apply the concept for various development organizations.

Technical Debt tracking: Current state of practice

The more dynamic and unpredictable the development constraints, the more agile the development project should be to cope with and utilize inherent change. Especially in such contexts, aligning with the project's mission and vision, committing to next steps, and documenting the development path is challenging. To support the decision making process of self-organized agile development teams with an overview, a recent research paper proposes the Progress Map. The investigation at hand applies the Progress Map in semi-industrial development projects to empirically validate its applicability and performance in the form of a multi-case study. The results indicate that, given future refinements, this instrument can be valuable to plan, document, and communicate the progress of an agile development project.

The practical implications of technical debt for the entire software life cycle; with examples and case studies. Technical debt in software is incurred when developers take shortcuts and make ill-advised technical decisions in the initial phases of a project, only to be confronted with the need for costly and labor-intensive workarounds later. This book offers advice on how to avoid technical debt, how to locate its sources, and how to remove it. It focuses on the practical implications of technical debt for the entire software lifecycle, with examples and case studies from companies that range from Boeing to Twitter. Technical debt is normal; it is part of most iterative development processes. But if debt is ignored, over time it may become unmanageably complex, requiring developers to spend all of their effort fixing bugs, with no time to add new features—and after all, new features are what customers really value. The authors explain how to monitor technical det, how to measure it, and how and when to pay it down. Broadening the conventional definition of technical debt, they cover requirements debt, implementation debt, testing debt, architecture debt, documentation debt, deployment debt, and social debt. They intersperse technical discussions with “Voice of the Practitioner” sidebars that detail real-world experiences with a variety of technical debt issues.

It is a conundrum that agile projects can succeed ‘without requirements’ when weak requirements engineering is a known cause for project failures. While Agile development projects often manage well without extensive requirements documentation, test cases are commonly used as requirements. We have investigated this agile practice at three companies in order to understand how test cases can fill the role of requirements. We performed a case study based on twelve interviews performed in a previous study. The findings include a range of benefits and challenges in using test cases for eliciting, validating, verifying, tracing and managing requirements. In addition, we identified three scenarios for applying the practice, namely as a mature practice, as a de facto practice and as part of an agile transition. The findings provide insights into how the role of requirements may be met in agile development including challenges to consider.

One major problem in using static analyzers to manage, monitor, control, and reason about technical debt is that industrial projects have a huge amount of technical debt which reflects hundreds of quality rule violations (e.g., high complex module or low comment density). Moreover the negative impact of violating quality rules (i.e., technical debt interest) may vary across rules or even across contexts. Thus, without a context-specific validation and prioritization of quality rules, developers cannot effectively manage technical debt. This paper reports on a case study aimed at exploring the interest associated with violating quality rules; i.e., we investigate if and which quality rules are important for software developers. Our empirical method consists of a survey and a quantitative analysis of the historical data of a CMMI Level 5 software company. The main result of the quantitative analysis is that classes violating several quality rules are five times more defect prone than classes not violating any rule. The main result of the survey is that some rules are perceived by developers as more important than others; however, there is no false positive (i.e., incorrect rule or null interest). These results pave the way to a better practical use of quality rules to manage technical debt and describe new research directions for building a scientific foundation to the technical debt metaphor.

Due to cost and time constraints, software quality is often neglected in the evolution and adaptation of software. Thus, maintainability suffers, maintenance costs rise, and the development takes longer. These effects are referred to as “technical debt”. The challenge for project managers is to find a balance when using the given budget and schedule, either by reducing technical debt or by adding technical features. This balance is needed to keep time to market for current product releases short and future maintenance costs at an acceptable level. Method: The project ProDebt aimed at developing an innovative methodology and a software tool to support the strategic planning of technical debt in the context of agile software development. In this project, we created quality models and collected corresponding measurement data for two case studies in two different companies. Altogether, the two case studies contributed 5–6 years of data, from the end of 2011, resp. mid-2012, until today. Using measurement and effort data, we trained a machine-learning model to predict productivity based on measurement data—representing the technical debt of a file at a given point in time. Result: We developed a prototype and a prediction model for forecasting potential savings based on proposed refactorings of key drivers of technical debt identified by the model. In this paper, we present the approach and the experiences made during model development.

Technical debt are those convenience tasks that developers perform to obtain short-term benefits but that in the future can produce activities that are difficult to perform or more costly. The impact of technical debt is perceived in all project resources, becoming a present and future problem in software development, affecting the quality of the software. This study uses a qualitative analysis, through interviews with a software development team, explores the knowledge and perception of technical debt, the practices used to manage it, the factors that influence its appearance and accumulation, the impact that development suffers, as well as the team and the maintainability of the software, as a result, ideas and solutions are proposed to address it effectively. The study was carried out with a qualitative investigative approach, which was based on the study of a single case with which the experiences and practices in the perception and management of technical debt are investigated. The data obtained reveal that technical debt affects the normal development of the development team and that it is present in the activities carried out by the team. Although the development team has a perception of the debt, it does not address it directly, and it is necessary to implement good practices to identify it and integrate it into the software development process. Addressing technical debt with the implementation of a formal process to manage it will ensure that scheduled time is controlled and resources are optimized, which will result in a culture of good practices that will ensure product quality, improving team performance and guaranteeing software maintainability

Agile practices have recently gained popularity among large number of companies as a mechanism for reducing cost and increasing ability to handle change in dynamic market conditions. Based on the principles of the Agile manifesto [1, 2], researchers and practitioners have proposed several software development approaches such as Extreme Programming, Scrum and Feature-Driven Development. These and other agile approaches have had significant impact on industrial software development practices. However, there is also a significant concern and perplexity about the role and importance of the issues related to a system’s software architecture, which is considered one of the most important initial design artefacts. It is argued that software architecture is an effective tool to cut development cost and time and to increase the quality of a system. Many practitioners of Agile approaches appear to view software architecture in the context of the plan-driven development paradigm [3]. For them, upfront design and evaluation of software architecture requires too much work, which may have very little value to the customers of a system. Hence, they perceive architectural work as part of high ceremony processes, which usually require large amount of documentation. We maintain that these two seemingly opposing views to software engineering can be integrated but it requires that experts from both fields work together to overcome evident challenges in bridging these two paradigms together. Indeed, software architecture researchers and practitioners appear to believe that sound architectural practices cannot be followed using Agile approaches. However, these two extreme views of Agile and architecture appear to neglect that many agile experts emphasises the importance of paying attention to good design and architecture early in the development process [4, 5]. Recently, there is growing recognition of the importance of paying more attention to architectural aspects in agile approaches [3, 6, 7]. We argue that there is a vital need for devising a research agenda for identifying and dealing with architecture-centric challenges in agile software development. Such research agenda should make it possible to guide the future research on integrating architecture-centric methods in agile approaches and give advice to the software industry on dealing with architecture related challenges. Some of the questions to stimulate discussion in the workshop are: What is the role of software architecture in Agile software development? What are the key architecture-centric challenges and potential solutions in Agile software development projects? What is the strength of the evidence that attention to architectural issues can be counterproductive in Agile development or vice versa? What are the prerequisites for integrating Architecture-Centric methods in agile development and potential implications of such integration?