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. © 2022 The Authors

Context: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.

Methods: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.

Results:We present the first comprehensive, structured, yet extendable taxonomy of assets, containing 57 types of assets.

Conclusions: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.

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. © 2020 IEEE.

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 144,476 code TDIs (35,372 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.

Software development is a collaborative endeavour. Organisations that develop software assign modules to different teams, i.e., teams own their modules and are responsible for them. These modules are rarely isolated, meaning that there exist dependencies among them. Therefore, other teams might often contribute to developing modules they do not own. The contribution can be, among other types, in the form of code authorship, code review, and issue detection. This research presents a model to investigate the alignment between module ownership and contribution and the preliminary results of an industrial case study to evaluate the model in practice. Our model uses seven metrics to assess teams' contributions. Initial results suggest that the model correctly identifies misalignment between ownership and contribution. The detection of misalignment between ownership and contribution is the first step towards investigating the impact it might have on the faster accumulation of Technical Debt. © 2022 IEEE.

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. © 2022 IEEE.