Related papers: SoK: Systematizing Software Artifacts Traceability…
Machine learning (ML) reproducibility is often framed as a problem of incomplete artifact recording. This framing leads to systems that prioritize capturing datasets, code, configurations, and execution environments. However, in…
There is an increasing global emphasis on information accessibility, with numerous researchers actively developing automated tools to detect and repair accessibility issues, thereby ensuring that individuals with diverse abilities can…
[Context.] The success of deep learning makes its usage more and more tempting in safety-critical applications. However such applications have historical standards (e.g., DO178, ISO26262) which typically do not envision the usage of machine…
The reproduction and replication of research results has become a major issue for a number of scientific disciplines. In computer science and related computational disciplines such as systems biology, the challenges closely revolve around…
In application domains that are regulated, software vendors must maintain traceability links between the regulatory items and the code base implementing them. In this paper, we present a traceability approach based on the intuition that the…
Background. Coping with the rapid growing complexity in contemporary software architecture, tracing has become an increasingly critical practice and been adopted widely by software engineers. By adopting tracing tools, practitioners are…
Purpose: Supply chain has become very complex today. There are multiple stakeholders at various points. All these stakeholders need to collaborate with each other in multiple directions for its effective and efficient management. The…
Traceability plays a vital role in facilitating various software development activities by establishing the traces between different types of artifacts (e.g., issues and commits in software repositories). Among the explorations for…
Practitioners are poorly supported by the scientific literature when managing traceability information models (TIMs), which capture the structure and semantics of trace links. In practice, companies manage their TIMs in very different ways,…
The field of deep learning has witnessed significant breakthroughs, spanning various applications, and fundamentally transforming current software capabilities. However, alongside these advancements, there have been increasing concerns…
The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis, such as testing and vulnerability detection. Such a large number…
Background: Software development results in the production of various types of artifacts: source code, version control system metadata, bug reports, mailing list conversations, test data, etc. Empirical software engineering (ESE) has…
Modern open source software development heavily relies on the issue tracking systems to manage their feature requests, bug reports, tasks, and other similar artifacts. Together, those "issues" form a complex network with links to each…
Background: The literature offers various methods for capturing software architectural knowledge (AK), including views, viewpoints, and architecture decision records (ADRs). In parallel, sustainability has gained prominence in software…
Being able to duplicate published research results is an important process of conducting research whether to build upon these findings or to compare with them. This process is called "replicability" when using the original authors'…
Requirements traceability plays an important role in ensuring software quality and responding to changes in requirements. Requirements trace links (such as the links between requirements and other software artifacts) underpin the modeling…
So far, the relationship between open science and software engineering expertise has largely focused on the open release of software engineering research insights and reproducible artifacts, in the form of open-access papers, open data, and…
Traceability is a key enabler of various activities in automotive software and systems engineering and required by several standards. However, most existing traceability management approaches do not consider that traceability is situated in…
Due to the growing complexity of software systems, there has been a dramatic increase and industry demand for tools and techniques on software refactoring in the last ten years, defined traditionally as a set of program transformations…
Sustainability is an increasingly-studied topic in software engineering in general, and in software architecture in particular. There are already a number of secondary studies addressing sustainability in software engineering, but no such…