Journal: Empirical Software Engineering

Abbreviation

Publisher

Springer

Journal Volumes

ISSN

1382-3256
1573-7616

Description

Filters

2014 - 201922020 - 20252
Reset filters

Search Results

Publications 1 - 4 of 4
  • Agile vs. structured distributed software development: A case study
    Item type: Journal Article
    Estler, Hans-Christian; Nordio, Martin; Furia, Carlo A.; et al. (2014)
    Empirical Software Engineering
    In globally distributed software development, does it matter being agile rather than structured? To answer this question, this paper presents an extensive case study that compares agile (Scrum, XP, etc.) vs. structured (RUP, waterfall) processes to determine if the choice of process impacts aspects such as the overall success and economic savings of distributed projects, the motivation of the development teams, the amount of communication required during development, and the emergence of critical issues. The case study includes data from 66 projects developed in Europe, Asia, and the Americas. The results show no significant difference between the outcome of projects following agile processes and structured processes, suggesting that agile and structured processes can be equally effective for globally distributed development. The paper also discusses several qualitative aspects of distributed software development such as the advantages of nearshore vs. offshore, the preferred communication patterns, and the effects on project quality.
  • Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net
    Item type: Journal Article
    Gote, Christoph; Scholtes, Ingo; Schweitzer, Frank (2021)
    Empirical Software Engineering
    Data from software repositories have become an important foundation for the empirical study of software engineering processes. A recurring theme in the repository mining literature is the inference of developer networks capturing e.g. collaboration, coordination, or communication from the commit history of projects. Many works in this area studied networks of co-authorship of software artefacts, neglecting detailed information on code changes and code ownership available in software repositories. To address this issue, we introduce git2net, a scalable python software that facilitates the extraction of fine-grained co-editing networks in large git repositories. It uses text mining techniques to analyse the detailed history of textual modifications within files. We apply our tool in two case studies using GitHub repositories of multiple Open Source as well as a proprietary software project. Specifically, we use data on more than 1.2 million commits and more than 25,000 developers to test a hypothesis on the relation between developer productivity and co-editing patterns in software teams. We argue that git2net opens up an important new source of high-resolution data on human collaboration patterns that can be used to advance theory in empirical software engineering, computational social science, and organisational studies.
  • From Aristotle to Ringelmann: a large-scale analysis of team productivity and coordination in Open Source Software projects
    Item type: Journal Article
    Scholtes, Ingo; Mavrodiev, Pavlin; Schweitzer, Frank (2016)
    Empirical Software Engineering
  • Simplifying software compliance: AI technologies in drafting technical documentation for the AI Act
    Item type: Journal Article
    Sovrano, Francesco; Hine, Emmie; Anzolut, Stefano; et al. (2025)
    Empirical Software Engineering
    The European AI Act has introduced specific technical documentation requirements for AI systems. Compliance with them is challenging due to the need for advanced knowledge of both legal and technical aspects, which is rare among software developers and legal professionals. Consequently, small and medium-sized enterprises may face high costs in meeting these requirements. In this study, we explore how contemporary AI technologies, including ChatGPT and an existing compliance tool (DoXpert), can aid software developers in creating technical documentation that complies with the AI Act. We specifically demonstrate how these AI tools can identify gaps in existing documentation according to the provisions of the AI Act. Using open-source high-risk AI systems as case studies, we collaborated with legal experts to evaluate how closely tool-generated assessments align with expert opinions. Findings show partial alignment, important issues with ChatGPT (3.5 and 4), and a moderate (and statistically significant) correlation between DoXpert and expert judgments, according to the Rank Biserial Correlation analysis. Nonetheless, these findings underscore the potential of AI to combine with human analysis and alleviate the compliance burden, supporting the broader goal of fostering responsible and transparent AI development under emerging regulatory frameworks.
Publications 1 - 4 of 4