Organizations often migrate monolithic architectures to microservices based on ad hoc data, expert opinions, or industry trends without assessing their specific context and needs. Such transitions tend to coincide with increased architectural complexity and technical debt (TD), making it crucial to understand how TD evolves over time in industrial settings to manage it effectively. This observational study explores the evolution of technical debt density (TDD) in a single software product consisting of both monolithic and microservice architectures at a Swedish fintech company, without aiming to establish causality between architectural styles and TDD trends. We further investigate TDD trends across various microservice size categories, team types, and the relationship between size and TDD. We analyzed SonarQube TD data collected from one monolith and 78 microservices from August 2022 to December 2024, and conducted semi-structured interviews with practitioners (a development manager, a product owner, and a lead developer) to validate and contextualize the quantitative findings. Our results show that, in this case, the monolithic system exhibits a decreasing TDD trend over time despite continued growth in size, while a gradual increase in TDD is observed across microservices. Furthermore, TDD trends appear inconsistent among small microservices, more consistently growing in medium-sized microservices, and comparatively stable in larger services. Differences in TDD trends are observed across services owned by platform teams and product teams. Overall, the findings from this specific case suggest that TDD evolves differently in monolith and microservices, highlighting the importance of continuous monitoring and context-aware interpretation of TDD trends in practice.

Background: Software companies must balance fast delivery and quality, a trade-off that often introduces technical debt and wastes developer's time. Technical debt tends to increase as software evolves, which is assumed to slow down development and maintenance activities. However, the potential relationship between technical debt and lead time lacks empirical evidence. Objective: This paper reports an empirical study to explore the potential relationship between technical debt and lead time in resolving Jira tickets. We further aim to measure the extent to which technical debt can explain the variation in lead time. Method: We conducted an industrial case study to explore this relationship in six components, each of which was analyzed individually. Technical debt was measured using SonarQube and normalized with the component's size. Lead times to resolve Jira tickets were collected from Jira and averaged monthly. Results: The study found little to no correlation between technical debt and lead time to resolve Jira tickets in five components, with technical debt explaining a variation in lead time ranging from 0% to 41%. However, it is less than 30% in most of the components. Conclusion: Technical debt alone does not fully explain the variation in lead time. There should be some other confounding variables (e.g., size and complexity of the changes, number of teams involved, priorities, component ownership) affecting lead time or a residual effect, i.e., interest, that might manifest later. Further investigation into those confounding variables is essential.

Abstract Context In software development organizations employing weak or collective ownership, different teams are allowed and expected to autonomously perform changes in various components. This creates diversity both in the knowledge of, and in the responsibility for, individual components. Objective Our objective is to understand how and why different teams introduce technical debt in the form of code clones as they change different components. Method We collected data about change size and clone introductions made by ten teams in eight components which was part of a large industrial software system. We then designed a Multi-Level Generalized Linear Model (MLGLM), to illustrate the teams’ differing behavior. Finally, we discussed the results with three development teams, plus line manager and the architect team, evaluating whether the model inferences aligned with what they expected. Responses were recorded and thematically coded. Results The results show that teams do behave differently in different components, and the feedback from the teams indicates that this method of illustrating team behavior can be useful as a complement to traditional summary statistics of ownership. Conclusions We find that our model-based approach produces useful visualizations of team introductions of code clones as they change different components. Practitioners stated that the visualizations gave them insights that were useful, and by comparing with an average team, inter-team comparisons can be avoided. Thus, this has the potential to be a useful feedback tool for teams in software development organizations that employ weak or collective ownership.

Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Although decision-making during requirements engineering has significant consequences, the debt metaphor in requirements engineering is inadequately explored. We aim to conceptualize how the debt metaphor applies to requirements engineering by organizing concepts related to practitioners’ understanding and managing of requirements engineering debt (RED). We conducted two in-depth expert interviews to identify key requirements engineering debt concepts and construct a survey instrument. We surveyed 69 practitioners worldwide regarding their perception of the concepts and developed an initial analytical theory. We propose a RED theory that aligns key concepts from technical debt research but emphasizes the specific nature of requirements engineering. In particular, the theory consists of 23 falsifiable propositions derived from the literature, the interviews, and survey results. The concepts of requirements engineering debt are perceived to be similar to their technical debt counterpart. Nevertheless, measuring and tracking requirements engineering debt are immature in practice. Our proposed theory serves as the first guide toward further research in this area.

While modern software companies strive to increase team autonomy to enable them to successfully operate the piece of software they develop and deploy, efficient ways to orchestrate the work of multiple autonomous teams working in parallel are still poorly understood. In this paper, we report how team autonomy is maintained at Spotify at scale, based on team retrospectives, interviews with team managers and archival analysis of corporate databases and work procedures. In particular, we describe how managerial authority is decentralized through various workgroups with collective authority, what compromises are made to team autonomy to ensure alignment and which team-related factors can further hinder autonomy. Our findings show that scaled autonomy at Spotify does not mean anarchy, or unlimited permissiveness. Instead, squads are expected to take responsibility for their work and coordinate, communicate and align their actions with others, and comply with a few enabling constraints. Further, squads take many decisions independently without management control or due to collective efforts that bypass formal boundary structures. Mechanisms and strategies that enable self-organization at Spotify are related to effective sharing of the codebase, achieving alignment, networking and knowledge sharing, and are described to guide other companies in their efforts to scale autonomy.

In early 2020, the Covid-19 pandemic forced employees in tech companies worldwide to abruptly transition from working in offices to working from their homes. During two years of predominantly working from home, employees and managers alike formed expectations about what post-pandemic working life should look like. Many companies are experimenting with new work policies that balance employee- and manager expectations regarding where, when and how work should be done in the future. In this article, we gather experiences of the new trend of remote working based on the synthesis of 22 company-internal surveys of employee preferences for WFH, and 26 post-pandemic work policies from 17 companies and their sites, covering 12 countries in total. Our results are threefold. First, through the new work policies, all companies formally give employees more flexibility regarding working time and location. Second, there is a great variation in how much flexibility the companies are willing to yield to the employees. The paper details the different formulations that companies adopted to document the extent of permitted WFH, exceptions, relocation permits and the authorisation procedures. Third, we document a change in the psychological contract between employees and managers, where the option of working from home is converted from an exclusive perk that managers could choose to give to the few, to a core privilege that all employees feel they are entitled to. Finally, there are indications that as the companies learn and solicit feedback regarding the efficiency of the chosen strategies, we will see further developments and changes in the work policies concerning how much flexibility to work whenever and from wherever they grant. Through these findings, the paper contributes to a growing literature about the new trends emerging from the pandemic in tech companies and spells out practical implications onwards.

The COVID-19 outbreak has admittedly caused interruptions to production, transportation, and mobility, therefore, having a significant impact on the global supply and demand chain's well-functioning. But what happened to companies developing digital services, such as software? How has the enforced Working-From-Home (WFH) mode impacted their ability to deliver software, if at all? This article shares our findings from monitoring the WFH during 2020 in an international software company with engineers located in Sweden, the USA, and the UK. We analyzed different aspects of productivity, such as developer job satisfaction and well-being, activity, communication and collaboration, efficiency and flow based on the archives of commit data, calendar invites, Slack communication, the internal reports of WFH experiences, and 30 interviews carried out in April/May and September 2020. We add more objective evidence to the existing COVID-19 studies the vast majority of which are based on self-reported productivity from the early months of the pandemic. We find that engineers continue committing code and carrying out their daily duties, as their routines adjust to "the new norm". Our key message is that software engineers can work from home and quickly adjust their tactical approaches to the changes of unprecedented scale. Further, WFH has its benefits, including better work-life balance, improved flow, and improved quality of distributed meetings and events. Yet, WFH is not challenge free: not everybody feels equally productive working from home, work hours for many increased, while physical activity, socialization, pairing and opportunities to connect to unfamiliar colleagues decreased. Information sharing and meeting patterns also changed. Finally, experiences gained during the pandemic will have a lasting impact on the future of the workplace. The results of an internal company-wide survey suggest that only 9% of engineers will return to work in the office full time. Our article concludes with the InterSoft's strategy for work from anywhere (WFX), and a list of useful adjustments for a better WFH.

Developing software-intensive products or services usually involves a plethora of software artefacts. Assets are artefacts intended to be used more than once and have value for organisations; examples include test cases, code, requirements, and documentation. During the development process, assets might degrade, affecting the effectiveness and efficiency of the development process. Therefore, assets are an investment that requires continuous management. Identifying assets is the first step for their effective management. However, there is a lack of awareness of what assets and types of assets are common in software-developing organisations. Most types of assets are understudied, and their state of quality and how they degrade over time have not been well-understood. We performed an analysis of secondary literature and a field study at five companies to investigate and identify assets to fill the gap in research. The results were analysed qualitatively and summarised in a taxonomy. We present the first comprehensive, structured, yet extendable taxonomy of assets, containing 57 types of assets. The taxonomy serves as a foundation for identifying assets that are relevant for an organisation and enables the study of asset management and asset degradation concepts.

Background: The COVID-19 outbreak interrupted regular activities for over a year in many countries and resulted in a radical change in ways of working for software development companies, i.e., most software development companies switched to a forced Working-From-Home (WFH) mode. Aim: Although several studies have analysed different aspects of forced WFH mode, it is unknown whether and to what extent WFH impacted the accumulation of technical debt (TD) when developers have different ways to coordinate and communicate with peers. Method: Using the year 2019 as a baseline, we carried out an industrial case study to analyse the evolution of TD in five components that are part of a large project while WFH. As part of the data collection, we carried out a focus group with developers to explain the different patterns observed from the quantitative data analysis. Results: TD accumulated at a slower pace during WFH as compared with the working-from-office period in four components out of five. These differences were found to be statistically significant. Through a focus group, we have identified different factors that might explain the changes in TD accumulation. One of these factors is responsibility diffusion which seems to explain why TD grows faster during the WFH period in one of the components. Conclusion: The results suggest that when the ways of working change, the change between working from office and working from home does not result in an increased accumulation of TD.

Internet of Things (IoT) is a growing technology that relies on connected ‘things’ that gather data from peer devices and send data to servers via APIs (Application Programming Interfaces). The design quality of those APIs has a direct impact on their understandability and reusability. This study focuses on the linguistic design quality of REST APIs for IoT applications and assesses their linguistic quality by performing the detection of linguistic patterns and antipatterns in REST APIs for IoT applications. Linguistic antipatterns are considered poor practices in the naming, documentation, and choice of identifiers. In contrast, linguistic patterns represent best practices to APIs design. The linguistic patterns and their corresponding antipatterns are hence contrasting pairs. We propose the SARAv2 (Semantic Analysis of REST APIs version two) approach to perform syntactic and semantic analyses of REST APIs for IoT applications. Based on the SARAv2 approach, we develop the REST-Ling tool and empirically validate the detection results of nine linguistic antipatterns. We analyse 19 REST APIs for IoT applications. Our detection results show that the linguistic antipatterns are prevalent and the REST-Ling tool can detect linguistic patterns and antipatterns in REST APIs for IoT applications with an average accuracy of over 80%. Moreover, the tool performs the detection of linguistic antipatterns on average in the order of seconds, i.e., 8.396 s. We found that APIs generally follow good linguistic practices, although the prevalence of poor practices exists.

During the development and maintenance of software-intensive products or services, we depend on various artefacts. Some of those artefacts, we deem central to the feasibility of a project and the product’s final quality. Typically, these central artefacts are referred to as assets. However, despite their central role in the software development process, little thought is yet invested into what eventually characterises as an asset, often resulting in many terms and underlying concepts being mixed and used inconsistently. A precise terminology of assets and related concepts, such as asset degradation, are crucial for setting up a new generation of cost-effective software engineering practices. In this position paper, we critically reflect upon the notion of assets in software engineering. As a starting point, we define the terminology and concepts of assets and extend the reasoning behind them. We explore assets’ characteristics and discuss what asset degradation is as well as its various types and the implications that asset degradation might bring for the planning, realisation, and evolution of software-intensive products and services over time. We aspire to contribute to a more standardised definition of assets in software engineering and foster research endeavours and their practical dissemination in a common, more unified direction.

Abstract Context: Technical debt (TD) discusses the negative impact of sub‐optimal decisions to cope with the need‐for‐speed in software development. Code technical debt items (TDI) are atomic elements of TD that can be observed in code artifacts. Empirical results on open‐source systems demonstrated how code‐smells, which are just one type of TDIs, are introduced and “survive” during release cycles. However, little is known about whether the results on the survivability of code‐smells hold for other types of code TDIs (i.e., bugs and vulnerabilities) and in industrial settings. Goal: Understanding the survivability of code TDIs by conducting an empirical study analyzing two industrial cases and 31 open‐source systems from Apache Foundation. Method: We analyzed 133,670 code TDIs (35,703 from the industrial systems) detected by SonarQube (in 193,196 commits) to assess their survivability using survivability models. Results: In general, code TDIs tend to remain and linger for long periods in open‐source systems, whereas they are removed faster in industrial systems. Code TDIs that survive over a certain threshold tend to remain much longer, which confirms previous results. Our results also suggest that bugs tend to be removed faster, while code smells and vulnerabilities tend to survive longer.

Context: The concept of software craftsmanship has early roots in computing, and in 2009, the Manifesto for Software Craftsmanship was formulated as a reaction to how the Agile methods were practiced and taught. But software craftsmanship has seldom been studied from a software engineering perspective. Objective: The objective of this article is to systematize an anatomy of software craftsmanship through literature studies and a longitudinal case study. Method: We performed a snowballing literature review based on an initial set of nine papers, resulting in 18 papers and 11 books. We also performed a case study following seven years of software development of a product for the financial market, eliciting qualitative, and quantitative results. We used thematic coding to synthesize the results into categories. Results: The resulting anatomy is centered around four themes, containing 17 principles and 47 hierarchical practices connected to the principles. We present the identified practices based on the experiences gathered from the case study, triangulating with the literature results. Conclusion: We provide our systematically derived anatomy of software craftsmanship with the goal of inspiring more research into the principles and practices of software craftsmanship and how these relate to other principles within software engineering in general.

Code evolution, whether related to the development of new features, bug fixing, or refactoring, inevitably changes the quality of the code. One particular type of such change is the accumulation of Technical Debt (TD) resulting from sub-optimal design decisions. Traditionally, refactoring is one of the means that has been acknowledged to help to keep TD under control. Developers refactor their code to improve its maintainability and to repay TD (e.g., by removing existing code smells and anti-patterns in the source code). While the accumulation of the TD and the effect of refactoring on TD have been studied before, there is a lack of empirical evidence from industrial projects on how the different types of code changes affect the TD and whether specific refactoring operations are more effective for repaying TD. To fill this gap, we conducted an empirical study on an industrial project and investigated how Refactoring, Bug Fixing, and New Development affect the TD. We have analyzed 2, 286 commits in total to identify which activities reduced, kept the same, or even increased the TD, further delving into specific refactoring operations to assess their impact. Our results suggest that TD in the studied project is mainly introduced in the development of new features (estimated in 72.8 hours). Counterintuitively, from the commits tagged as refactoring, only 22.90% repay TD (estimated to repay 8.30 hours of the TD). Moreover, while some types of refactoring operations (e.g., Extract Method), help repaying TD, other refactoring operations (e.g., Move Class) are highly prone to introduce more TD.

Context The micro-services architectural pattern advocates for the partitioning of functionality into loosely coupled services, which should be backward compatible, to enable independent upgrades. Deprecation is commonly used as a tool to manage multiple versions of methods or services. However, deprecation carries a cost in that tests might be duplicated and might rely on services that have become deprecated over time.

With the growing interest of adopting agile methods in offshored process, many companies realized that the use of agile methods and practices in companies located outside the location of early adopters of agile methods may be challenging. India, the main destination of offshoring contracts, have received particular attention, due to the big cultural differences. Critical analysis of related studies suggests that impeding behaviors are mostly rooted in the hierarchical culture of Indian organizations and related management behavior of command-and-control. But what happens in distributed projects with a more empowering onshore management? In this paper, we present the findings from a multiple-case study of DevOps teams with members from a mature agile company located in Sweden and a more hierarchical offshore vendor from India. Based on two focus groups we list culturally different behaviors of offshore engineers that were reported to impede agile ways of working. Furthermore, we report the findings from surveying 36 offshore team members from five DevOps teams regarding their likely behavior in situations reported to be problematic. Our findings confirm a number of previously reported behaviors rooted in cultural differences that impede the adoption of agile ways of working when collaborating with offshore engineers. At the same time, our survey results suggest that among the five surveyed teams there were teams that succeeded with the cultural integration of the offshore team members. Finally, our findings demonstrate the importance of cultural training especially when onboarding new team members.

Global software engineering has changed the way software is developed today. To address the new challenges, many universities have launched specially tailored courses to train young professionals to work in globally distributed projects. However, a mere acknowledgment of the geographic, temporal, and cultural differences does not necessarily lead to a deep understanding of the underlying practical implications. Therefore, many universities developed alternative teaching and learning activities, such as multi-university collaborative projects and small-scale simulations or games. In this article, we present a small-scale exercise that uses LEGO bricks to teach skills necessary for global work. We describe the many different interventions that could be implemented in the execution of the exercise. We had seven runs of the exercises and report our findings from executing seven runs of the exercise with the total of 104 students from five different courses in two different universities. Our results suggest that the exercise can be a valuable tool to help students dealing with troublesome knowledge associated with global software engineering and a useful complement to the courses dedicated to this subject.

Summary Service‐based cloud applications are software systems that continuously evolve to satisfy new user requirements and technological changes. This kind of applications also require elasticity, scalability, and high availability, which means that deployment of new functionalities or architectural adaptations to fulfill service level agreements (SLAs) should be performed while the application is in execution. Dynamic architectural reconfiguration is essential to minimize system disruptions while new or modified services are being integrated into existing cloud applications. Thus, cloud applications should be developed following principles that support dynamic reconfiguration of services, and also tools to automate these reconfigurations at runtime are needed. This paper presents an extension of a model‐driven method for dynamic and incremental architecture reconfiguration of cloud services that allows developers to specify new services as software increments, and the tool to generate the implementation code for the services integration logic and the deployment and architectural reconfiguration scripts specific to the cloud environment in which the service will be deployed (e.g., Microsoft Azure). We also report the results of a quasi‐experiment that empirically validate our method. It was conducted to evaluate their perceived ease of use, perceived usefulness, and perceived intention to use. The results show that the participants perceive the method to be useful, and they also expressed their intention to use the method in the future. Although further experiments must be carried out to corroborate these results, the method has proven to be a promising architectural reconfiguration process for cloud applications in the context of agile and incremental development processes. Copyright © 2016 John Wiley & Sons, Ltd.

Technical Debt (TD) is a metaphor used to explain the negative impacts that sub-optimal design decisions have in the long-term perspective of a software project. Although TD is acknowledged by both researchers and practitioners to have strong negative impact on Software development, its study on Testware has so far been very limited. A gap in knowledge that is important to address due to the growing popularity of Testware (scripted automated testing) in software development practice.In this paper we present a mapping analysis that connects 21 well-known, Software, object-oriented TD items to Testware, establishing them as Testware Technical Debt (TTD) items. The analysis indicates that most Software TD items are applicable or observable as TTD items, often in similar form and with roughly the same impact as for Software artifacts (e.g. reducing quality of the produced artifacts, lowering the effectiveness and efficiency of the development process whilst increasing costs). In the analysis, we also identify three types of connections between software TD and TTD items with varying levels of impact and criticality. Additionally, the study finds support for previous research results in which specific TTD items unique to Testware were identified. Finally, the paper outlines several areas of future research into TTD.

Identifier lexicon may have a direct impact on software understandability and reusability and, thus, on the quality of the final software product. Understandability and reusability are two important characteristics of software quality. REpresentational State Transfer (REST) style is becoming a de facto standard adopted by software organizations to build their Web applications. Understandable and reusable Uniform Resource Identifers (URIs) are important to attract client developers of RESTful APIs because good URIs support the client developers to understand and reuse the APIs. Consequently, the use of proper lexicon in RESTful APIs has also a direct impact on the quality of Web applications that integrate these APIs. Linguistic antipatterns represent poor practices in the naming, documentation, and choice of identifiers in the APIs as opposed to linguistic patterns that represent the corresponding best practices. In this paper, we present the Semantic Analysis of RESTful APIs (SARA) approach that employs both syntactic and semantic analyses for the detection of linguistic patterns and antipatterns in RESTful APIs. We provide detailed definitions of 12 linguistic patterns and antipatterns and define and apply their detection algorithms on 18 widely-used RESTful APIs, including Facebook, Twitter, and Dropbox. Our detection results show that linguistic patterns and antipatterns do occur in major RESTful APIs in particular in the form of poor documentation practices. Those results also show that SARA can detect linguistic patterns and antipatterns with higher accuracy compared to its state-of-the-art approach — DOLAR.

Flexible processes emerged to provide flexibility to business process execution. A flexible process is not static and can have several different executions, that is influenced by the current situation. In this context, the decision-making is placed in the hands of any knowledge worker during the execution, who decides which tasks and in which order they will be executed. Two approaches for flexible processes are discussed in this paper: case management and declarative processes. In particular we use the CMMN standard and the ConDec language for the two approaches, respectively. We compare them based on scope, model representation, formal semantics, and limitations. Our goal is to present commonalities and differences between the languages in order to identify potential extensions to make them more complete to attain more flexible process examples.