Rahul Pandita

Matteo Paltenghi, Rahul Pandita, Austin Z. Henley et al.

Recent neural models of code, such as OpenAI Codex and AlphaCode, have demonstrated remarkable proficiency at code generation due to the underlying attention mechanism. However, it often remains unclear how the models actually process code, and to what extent their reasoning and the way their attention mechanism scans the code matches the patterns of developers. A poor understanding of the model reasoning process limits the way in which current neural models are leveraged today, so far mostly for their raw prediction. To fill this gap, this work studies how the processed attention signal of three open large language models - CodeGen, InCoder and GPT-J - agrees with how developers look at and explore code when each answers the same sensemaking questions about code. Furthermore, we contribute an open-source eye-tracking dataset comprising 92 manually-labeled sessions from 25 developers engaged in sensemaking tasks. We empirically evaluate five heuristics that do not use the attention and ten attention-based post-processing approaches of the attention signal of CodeGen against our ground truth of developers exploring code, including the novel concept of follow-up attention which exhibits the highest agreement between model and human attention. Our follow-up attention method can predict the next line a developer will look at with 47% accuracy. This outperforms the baseline prediction accuracy of 42.3%, which uses the session history of other developers to recommend the next line. These results demonstrate the potential of leveraging the attention signal of pre-trained models for effective code exploration.

Razvan Mihai Popescu, David Gros, Andrei Botocan et al.

The rise of large language models for code has reshaped software development. Autonomous coding agents, able to create branches, open pull requests, and perform code reviews, now actively contribute to real-world projects. Their growing role offers a unique and timely opportunity to investigate AI-driven contributions and their effects on code quality, team dynamics, and software maintainability. In this work, we construct a novel dataset of approximately $110,000$ open-source pull requests, including associated commits, comments, reviews, issues, and file changes, collectively representing millions of lines of source code. We compare five popular coding agents, including OpenAI Codex, Claude Code, GitHub Copilot, Google Jules, and Devin, examining how their usage differs in various development aspects such as merge frequency, edited file types, and developer interaction signals, including comments and reviews. Furthermore, we emphasize that code authoring and review are only a small part of the larger software engineering process, as the resulting code must also be maintained and updated over time. Hence, we offer several longitudinal estimates of survival and churn rates for agent-generated versus human-authored code. Ultimately, our findings indicate an increasing agent activity in open-source projects, although their contributions are associated with more churn over time compared to human-authored code.

Agnieszka Ciborowska, Aleksandar Chakarov, Rahul Pandita

Mainframe systems are facing a critical shortage of developer workforce as the current generation of COBOL developers retires. Furthermore, due to the limited availability of public COBOL resources, entry-level developers, who assume the mantle of legacy COBOL systems maintainers, face significant difficulties during routine maintenance tasks, such as code comprehension and defect location. While we made substantial advances in the field of software maintenance for modern programming languages yearly, mainframe maintenance has received limited attention. With this study, we aim to direct the attention of researchers and practitioners towards investigating and addressing challenges associated with mainframe development. Specifically, we explore the scope of defects affecting COBOL systems and defect location strategies commonly followed by COBOL developers and compare them with the modern programming language counterparts. To this end, we surveyed 30 COBOL and 74 modern Programming Language (PL) developers to understand the differences in defects and defect location strategies employed by the two groups. Our preliminary results show that: (1) major defect categories affecting the COBOL ecosystem are different than defects encountered in modern PL software projects; (2) the most challenging defect types in COBOL are also the ones that occur most frequently; and (3) COBOL and modern PL developers follow similar strategies to locate defective code.