Antonio Martini

In recent years in the software industry, the use of safety-critical software is increasing at a rapid rate. However, little is known about the relationship between safety-critical regulations and the management of technical debt. The research is based on interviews with 19 practitioners working in different safety-critical domains implementing software according to different safety regulation standards. The results are three-fold. First, the result shows that performing technical debt refactoring tasks in safety-critical software requires several additional activities and costs, compared to non-safety-critical software. This study has also identified several negative effects due to the impact of these regulatory requirements. Second, the results show that the safety-critical regulations strengthen the implementation of both source code and architecture and thereby initially limit the introduction of technical debt. However, at the same time, the regulations also force the software companies to perform later suboptimal work-around solutions that are counterproductive in achieving a high-quality software since the regulations constrain the possibility of performing optimal TD refactoring activities. Third, the result shows that technical debt refactoring decisions are heavily weighed on the costs associated with the application's recertification process and that these decisions seldom include the benefits of the refactoring activities in a structured way.

Remediation of technical debt through regular refactoring initiatives is considered vital for the software system's long and healthy life. However, since today's software companies face increasing pressure to deliver customer value continuously, the balance between spending developer time, effort, and resources on implementing new features or spending it on refactoring of technical debt becomes vital. The goal of this study is to explore how the prioritization of technical debt is carried out by practitioners within today's software industry. This study also investigates what factors influence the prioritization process and its related challenges. This paper reports the results of surveying 17 software practitioners, together with follow-up interviews with them. Our results show that there is no uniform way of prioritizing technical debt and that it is commonly done reactively without applying any explicit strategies. Often, technical debt issues are managed and prioritized in a shadow backlog, separate from the official sprint backlog. This study was also able to identify several different challenges related to prioritizing technical debt, such as the lack of quantitative information about the technical debt items and that the refactoring of technical debt issues competes with the implementation of customer requirements.

Architectural technical debt can be generated by changes in the business and the environment of an organization. In this paper, we emphasize the change in scalability requirements due to new regulations. Scalability is the ability of a system to handle an increased workload. For complex systems that are abruptly exposed via open interfaces and hence a greater workload, the scalability requirements may quickly increase, leading to technical debt. We term this scalability debt. This paper describes scalability triage, a light-weight, novel technique for identifying scalability threats as a form of technical debt. We illustrate this technique with an open banking case from a large software organization. Open banking is partly caused by the new European PSD2 regulative that enforce banks to open interfaces to unknown third-party actors. Banking systems are well-established, mature systems. However, with the advent of open banking and PSD2, the workload may quickly rocket. This leads to tougher scalability requirements and accumulated architectural debt, despite previously sound architectural decisions. Using scalability triage, such risks may be identified fast. It will then be possible to prevent this form of technical debt with timely reengineering.

The concept of technical debt has been explored from many perspectives but its precise estimation is still under heavy empirical and experimental inquiry. We aim to understand whether, by harnessing approximate, data-driven, machine-learning approaches it is possible to improve the current techniques for technical debt estimation, as represented by a top industry quality analysis tool such as SonarQube. For the sake of simplicity, we focus on relatively simple regression modelling techniques and apply them to modelling the additional project cost connected to the sub-optimal conditions existing in the projects under study. Our results shows that current techniques can be improved towards a more precise estimation of technical debt and the case study shows promising results towards the identification of more accurate estimation of technical debt.

Large-scale agile projects bring inter-teams interaction challenges. Teams need to be autonomous, but often crosscutting concerns affect many teams. If the teams fail to collaborate on these concerns, the negative effects might hinder agility in the medium and long term. In other words, the organization and the system accumulate debt, on which the teams pay a high interest. Such debt must therefore be prioritized and “repaid” timely. We conducted a case study with interviews, observations and document analysis. Via both team- and large-scale retrospectives we investigated how teams coordinate and discuss Technical-, Social- and Process Debts.

Introduction: Software companies aim to achieve continuous delivery to constantly provide value to their customers. A popular strategy is to use microservices architecture. However, such an architecture is also subject to debt, which hinders the continuous delivery process and thus negatively affects the software released to the customers. Objectives: The aim of this study is to identify issues, solutions and risks related to Architecture Technical Debt in microservices. Method: We conducted an exploratory case study of a real life project with about 1000 services in a large, international company. Through qualitative analysis of documents and interviews, we investigated Architecture Technical Debt in the communication layer of a system with microservices architecture. Results: Our main contributions are a list of Architecture Technical Debt issues specific for the communication layer in a system with microservices architecture, as well as their associated negative impact (interest), a solution to repay the debt and the its cost (principal). Among the found Architecture Technical Debt issues were the existence of business logic in the communication layer and a high amount of point-to-point connections between services. The studied solution consists of the implementation of different canonical models specific to different domains, the removal of business logic from the communication layer, and migration from services to use the communication layer correctly. We also contributed with a list of possible risks that can affect the payment of the debt, as lack of funding and inadequate prioritization. Conclusion: We found issues, solutions and possible risks that are specific for microservices architectures not yet encountered in the current literature. Our results may be useful for practitioners that want to avoid or repay Technical Debt in their microservices architecture.

Continuous integration is believed by many to improve software quality, including cyclomatic complexity. In this paper an exploratory study investigates the relationship between continuous integration behavior among developers and the cyclomatic complexity - particularly at critical levels - of the source code they commit, as a correct understanding of the consequences of a software development practice, particularly one as popular as continuous integration, is of crucial importance to industry professionals when deciding on improvement efforts. It is found that developer behavior differs significantly, as does their knowledge and awareness of cyclomatic complexity as a concept. We find that even though large commits ("big bangs") contribute proportionately slightly more to cyclomatic complexity, the effect is much less pronounced for critical cyclomatic complexity. It is also found that complex code tends to overlap with high levels of change activity, and that some developers deliberately change their integration behavior in the face of such hotspots, as a strategy to avoid merge conflicts.