When developing and maintaining software-intensive products or services, we often depend on various "assets", denoting the inherent value to selected artefacts when carrying out development and maintenance activities. When exploring various areas in Software Engineering, such as Technical Debt and our work with industry partners, we soon realised that many terms and concepts are frequently intermixed and used inconsistently. Despite the central role of assets to software engineering, management, and evolution, little thoughts are yet invested into what assets eventually are. A clear terminology of "assets" and related concepts, such as "value" or "value degradation", just to name two, are crucial for setting up effective software engineering practices.

As a starting point for our own work, we had to define the terminology and concepts, and extend the reasoning around the concepts. In this position paper, we critically reflect upon the resulting notion of Assets in Software Engineering. We explore various types of assets, their main characteristics, such as providing inherent value. We discuss various types of value degradation and the possible implications of this on the planning, realisation, and evolution of software-intensive products and services over time. 

Developing a software-intensive product or service can be a significant undertaking, associated with unique challenges in each project stage, from inception to development, delivery, maintenance, and evolution. Each step results in artefacts that are crucial for the project outcome, such as source-code and supporting deliverables, e.g., documentation.

Artefacts which have inherent value for the organisation are assets, and as assets, they are subject to degradation. This degradation occurs over time, as artefacts age, and can be more immediate or slowly over a period of time, similar to the concept of technical debt. One challenge with the concept of assets is that it seems not to be well-understood and generally delimited to a few types of assets (often code-based), overlooking other equally important assets. 

To bridge this gap, we have performed a study to formulate a structured taxonomy of assets. We use empirical data collected through industrial workshops and a literature review to ground the taxonomy. The taxonomy serves as foundations for concepts like asset degradation and asset management. The taxonomy can help contextualise, homogenise, extend the concept of technical debt, and serves as a conceptual framework for better identification, discussion, and utilisation of assets.

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.