The rapid adoption of AI-powered coding assistants is transforming software development practices, yet systematic comparisons of their effectiveness across different task types and over time remain limited. This paper presents an empirical study comparing five popular agents (OpenAI Codex, GitHub Copilot, Devin, Cursor, and Claude Code), analyzing 7,156 pull requests (PRs) from the AIDev dataset. Temporal trend analysis reveals heterogeneous evolution patterns: Devin exhibits the only consistent positive trend in acceptance rate (+0.77% per week over 32 weeks), whereas other agents remain largely stable. Our analysis suggests that the PR task type is a dominant factor influencing acceptance rates: documentation tasks achieve 82.1% acceptance compared to 66.1% for new features—a 16 percentage point gap that exceeds typical inter-agent variance for most tasks. OpenAI Codex achieves consistently high acceptance rates across all nine task categories (59.6%–88.6%), with stratified Chi-square tests confirming statistically significant advantages over other agents in several task categories. However, no single agent performs best across all task types: Claude Code leads in documentation (92.3%) and features (72.6%), while Cursor excels in fix tasks (80.4%).

We call a program that passes existing tests but still contains bugs as a buggy plausible program. Bugs in such a program can bypass the testing environment and enter the production environment, causing unpredictable consequences. Therefore, discovering and fixing such bugs is a fundamental and critical problem. However, no existing bug dataset is purposed to collect this kind of bug, posing significant obstacles to relevant research. To address this gap, we introduce TrickyBugs, a bug dataset with 3,043 buggy plausible programs sourced from human-written submissions of 324 real-world competition coding tasks. We identified the buggy plausible programs from approximately 400,000 submissions, and all the bugs in TrickyBugs were not previously detected. We hope that TrickyBugs can effectively facilitate research in the fields of automated program repair, fault localization, test generation, and test adequacy.

Deep learning (DL) models are being widely deployed in real-world applications, but their usage remains computationally intensive and energy-hungry. While prior work has examined model-level energy usage, the energy footprint of the DL frameworks, such as TensorFlow and PyTorch, used to train and build these models, has not been thoroughly studied. We present Greenlight, a large-scale dataset containing fine-grained energy profiling information of 1284 TensorFlow API calls. We developed a command line tool called CodeGreen to curate such a dataset. CodeGreen is based on our previously proposed framework FECoM, which employs static analysis and code instrumentation to isolate invocations of Tensor-Flow operations and measure their energy consumption precisely. By executing API calls on representative workloads and measuring the consumed energy, we construct detailed energy profiles for the APIS. Several factors, such as input data size and the type of operation, significantly impact energy footprints. Greenlight provides a ground-truth dataset capturing energy consumption along with relevant factors such as input parameter size to take the first step towards optimization of energy-intensive TensorFlow code. The Greenlight dataset opens up new research directions such as predicting API energy consumption, automated optimization, modeling efficiency trade-offs, and empirical studies into energy-aware DL system design.

Agile software development is nowadays a widely adopted practise in both open-source and industrial software projects. Agile teams typically heavily rely on issue management tools to document new issues and keep track of outstanding ones, in addition to storing their technical details, effort estimates, assignment to developers, and more. Previous work utilised the historical information stored in issue management systems for various purposes; however, when researchers make their empirical data public, it is usually relevant solely to the study's objective. In this paper, we present a more holistic and versatile dataset containing a wealth of information on more than half a million issues from 44 open-source Agile software, making it well-suited to several research avenues, and cross-analyses therein, including effort estimation, issue prioritization, issue assignment and many more. We make this data publicly available on GitHub to facilitate ease of use, maintenance, and extensibility.

Many papers on App Store Mining are susceptible to the App Sampling Problem, which exists when only a subset of apps are studied, resulting in potential sampling bias. We introduce the App Sampling Problem, and study its effects on sets of user review data. We investigate the effects of sampling bias, and techniques for its amelioration in App Store Mining and Analysis, where sampling bias is often unavoidable. We mine 106,891 requests from 2,729,103 user reviews and investigate the properties of apps and reviews from 3 different partitions: the sets with fully complete review data, partially complete review data, and no review data at all. We find that app metrics such as price, rating, and download rank are significantly different between the three completeness levels. We show that correlation analysis can find trends in the data that prevail across the partitions, offering one possible approach to App Store Analysis in the presence of sampling bias.