When two or more code snippets are identical or sufficiently similar, they form code clones. Such duplication can harm system maintainability as the software evolves. Code clone genealogy (CCG) extraction involves analyzing successive versions of a software system to identify code clones, their modifications, additions, and removals. Visualizing clone genealogies helps developers manage their clones, improving code comprehensibility and maintainability. Despite their importance, to the best of our knowledge, no fully functional, easily executable clone genealogy extractor exists. Furthermore, all extractors proposed in the literature are specifically designed to work with a particular set of clone detectors, resulting in strong coupling. To address these shortcomings, this paper presents OmniCCG, a code clone genealogy extractor that is agnostic to clone detectors. Given a Git repository and user settings, OmniCCG extracts code clone genealogies from the repository, along with common genealogy metrics, such as clone density, k-volatile, and others. Moreover, one may use OmniCCG in two different ways. The first is via a modern and responsive user interface, in which one can easily track the genealogies in their repository alongside a dashboard of relevant metrics. The second is via a console application that supports local execution. OmniCCG is available as an web application [27] and console application [28].

Code clones consist of two or more identical or similar code snippets. Code clones hurt maintainability by requiring synchronized updates across multiple locations and increasing the risk of inconsistent changes. To understand how clones evolve, the genealogy of code clones captures the evolutionary history of duplicated code snippets by linking them across successive versions of the software system. Since the emergence of Large Language Models (LLMs), software engineering has been reshaped, with code written and evolved differently. This evolution has given rise to coding agents who act as partners to developers. While code clone genealogy is well understood in human-centric development, its evolution in human–agent collaborative projects remains unclear. In this study, we analyze 350 code clone lineages across 6 software projects in which human actively colaborate with coding agents. We observed that humans introduce 85.71% of code clones, whereas agents contribute only 14.29%. Despite similar clone survival rates for both humans (80%) and agents (76%), the maintenance dynamics differ significantly. The analysis of genealogies reveals that humans predominate in maintaining lineages created by agents. These findings highlight that humans remain critical for the evolution of code generated by coding agents.

Throughout the history of software, evolution has occurred in cycles of rise and fall driven by competition, and open-source software (OSS) is no exception. This cycle is accelerating, particularly in rapidly evolving domains such as web development and deep learning. However, the impact of competitive relationships among OSS projects on their survival remains unclear, and there are risks of losing a competitive edge to rivals. To address this, this study proposes a new automated method called “Mutual Impact Analysis of OSS (MIAO)” to quantify these competitive relationships. The proposed method employs a structural vector autoregressive model and impulse response functions, normally used in macroeconomic analysis, to analyze the interactions among OSS projects. In an empirical analysis involving mining and analyzing 187 OSS project groups, MIAO identified projects that were forced to cease development owing to competitive influences with up to 81% accuracy, and the resulting features supported predictive experiments that anticipate cessation one year ahead with up to 77% accuracy. This suggests that MIAO could be a valuable tool for OSS project maintainers to understand the dynamics of OSS ecosystems and predict the rise and fall of OSS projects.

The rapid adoption of AI coding agents is fundamentally shifting software developers’ roles from code authors to code reviewers. While developers spend a significant portion of their time reading and comprehending code, the linguistic proficiency and complexity of the Python code generated by these agents remain largely unexplored. This study investigates the code proficiency of AI agents to determine the skill level required for developers to maintain their code. Leveraging the AIDev dataset, we mined 591 pull requests containing 5,027 Python files generated by three distinct AI agents and employed pycefr, a static analysis tool that maps Python constructs to six proficiency levels, ranging from A1 (Basic) to C2 (Mastery), to analyze the code. Our results reveal that: AI agents predominantly generate Basic-level code, with over 90% of constructs falling into the A1 and A2 categories, and less than 1% classified as Mastery (C2); AI agents’ and humans’ pull requests share a broadly similar proficiency profile; High-proficiency code by AI agents are from feature addition and bug fixing tasks. These findings suggest that while AI-generated code is generally accessible to developers with basic Python skills, specific tasks may require advanced proficiency to review and maintain complex, agent-generated constructs.

Code clones are fragments of code that are copied and reused within the same or across different codebases, often with minor modifications. Their presence poses significant challenges, as defects or changes in one cloned fragment may require consistent updates across all related clones, negatively affecting software maintainability. Code Clone Lifecycle analysis provides valuable insights into when code clones are introduced and how they evolve during the code review process. Recent advances in Large Language Models (LLMs) have enabled Coding Agents that autonomously create branches, modify code, and submit Pull Requests (PRs). While these agents improve productivity, they also introduce new challenges for managing code clones within PRs. This paper presents an analysis of the Code Clone Lifecycle in agentic PRs hosted on GitHub. Using the NiCad clone detection tool, we analyzed 7,851 PRs created by AI agents from the AiDev dataset. Our results identify 28,425 clones across 497 PRs. Manual validation of a representative sample shows a predominance of Type I (29%) and Type III (46.26%) clones. Among the affected PRs, 93 contain clones restricted to a single commit, 320 exhibit clones recurring across multiple commits, and 84 present both single and recurring occurrences. Overall, the findings indicate that clones tend to persist once introduced, progressing through the PR lifecycle and ultimately being merged into the codebase.

This paper presents a technique to identify a developer based on their IDE event data. We exploited the KaVE data set which recorded IDE activities from 85 developers with 11M events. We found that using an SVM with a linear kernel on raw event count outperformed k-NN in identifying developers with an accuracy of 0.52. Moreover, after setting the optimal number of events and sessions to train the classifier, we achieved a higher accuracy of 0.69 and 0.71 respectively. The findings shows that we can identify developers based on their IDE event data. The technique can be expanded further to group similar developers for IDE feature recommendations.