Abdul Ali Bangash

Muhammad Ahmad Khan, Hasnain Ali, Muneeb Rana et al.

AI coding agents are increasingly contributing to software development, yet their impact on mobile development has received little empirical attention. In this paper, we present the first category-level empirical study of agent-generated code in open-source mobile app projects. We analyzed PR acceptance behaviors across mobile platforms, agents, and task categories using 2,901 AI-authored pull requests (PRs) in 193 verified Android and iOS open-source GitHub repositories in the AIDev dataset. We find that Android projects have received 2x more AI-authored PRs and have achieved higher PR acceptance rate (71%) than iOS (63%), with significant agent-level variation on Android. Across task categories, PRs with routine tasks (feature, fix, and ui) achieve the highest acceptance, while structural changes like refactor and build achieve lower success and longer resolution times. Furthermore, our evolution analysis shows improvement in PR resolution time on Android through mid-2025 before it declined again. Our findings offer the first evidence-based characterization of AI agents effects on OSS mobile projects and establish empirical baselines for evaluating agent-generated contributions to design platform aware agentic systems.

Zhimin Zhao, Abdul Ali Bangash, Filipe Roseiro Côgo et al.

Foundation models (FM), such as large language models (LLMs), which are large-scale machine learning (ML) models, have demonstrated remarkable adaptability in various downstream software engineering (SE) tasks, such as code completion, code understanding, and software development. As a result, FM leaderboards have become essential tools for SE teams to compare and select the best third-party FMs for their specific products and purposes. However, the lack of standardized guidelines for FM evaluation and comparison threatens the transparency of FM leaderboards and limits stakeholders' ability to perform effective FM selection. As a first step towards addressing this challenge, our research focuses on understanding how these FM leaderboards operate in real-world scenarios ("leaderboard operations") and identifying potential pitfalls and areas for improvement ("leaderboard smells"). In this regard, we collect up to 1,045 FM leaderboards from five different sources: GitHub, Hugging Face Spaces, Papers With Code, spreadsheet and independent platform, to examine their documentation and engage in direct communication with leaderboard operators to understand their workflows. Through card sorting and negotiated agreement, we identify five distinct workflow patterns and develop a domain model that captures the key components and their interactions within these workflows. We then identify eight unique types of leaderboard smells in LBOps. By mitigating these smells, SE teams can improve transparency, accountability, and collaboration in current LBOps practices, fostering a more robust and responsible ecosystem for FM comparison and selection.

Zhimin Zhao, Zehao Wang, Abdul Ali Bangash et al.

Evaluation harnesses are software systems that orchestrate model evaluation by managing model invocation, data loading, metric computation, and result reporting. Despite their critical role in machine learning infrastructure, their operational challenges and engineering concerns have received limited attention so far. We present an empirical study of 57 evaluation harnesses, deriving a five-stage harness model and classifying 16,560 issues by workflow stage and root cause. Most harness operational challenges concentrate in the Specification stage (41.4% of issues), where harnesses integrate external models, datasets, and scoring judges. The three most frequent root causes of operational challenges are unimplemented features (24.3%), documentation gaps (20.3%), and missing input validation (17.2%), which together account for 61.7% of classified issues, spanning both defects in existing functionality and capability gaps that block intended workflows. Root causes also vary by workflow stage: environment incompatibility and external dependency breakage account for 36.2% of provisioning issues, whereas algorithmic error (25.9%) and validation gap (22.5%) dominate assessment issues. Together, these contributions establish an empirical foundation for treating evaluation engineering as a distinct software engineering concern.

Syed Muhammad Ashhar Shah, Sehrish Habib, Muizz Hussain et al.

Continuous Integration and Deployment (CI/CD) workflows are central to modern software delivery, yet the reliability of agentic AI bots operating within these workflows remain underexplored. Using pull requests (PRs), commits, and repositories from the AIDev dataset, we retrieved associated CI/CD workflow runs via the GitHub Actions API and analyzed 61,837 runs from 2,355 repositories, all triggered by PRs generated by five AI bots: Claude, Devin, Cursor, Copilot, and Codex. We observed substantial agent-dependent differences in workflow reliability, with Copilot and Codex achieving the highest success rates ~93% and ~94% respectively. At the repository level, we find a negative correlation between AI agent contribution frequency and workflow success rate, suggesting that a higher frequency of Agentic PRs may hinder CI/CD workflow reliability. We defined a taxonomy of 13 categories against 3,067 agentic PRs whose associated workflows failed, and observed a trend analysis that indicates visually observable shifts from functional to non-functional PR categories over time, although these trends are not statistically significant. Our findings motivate the need for actionable guidance on integrating AI agents into CI/CD workflows and prioritizing safeguards in workflows where failures are most likely to occur.

Hao Li, Hicham Masri, Filipe R. Cogo et al.

The rapid adoption of foundation models (e.g., large language models) has given rise to promptware, i.e., software built using natural language prompts. Effective management of prompts, such as organization and quality assurance, is essential yet challenging. In this study, we perform an empirical analysis of 24,800 open-source prompts from 92 GitHub repositories to investigate prompt management practices and quality attributes. Our findings reveal critical challenges such as considerable inconsistencies in prompt formatting, substantial internal and external prompt duplication, and frequent readability and spelling issues. Based on these findings, we provide actionable recommendations for developers to enhance the usability and maintainability of open-source prompts within the rapidly evolving promptware ecosystem.

Zhimin Zhao, Yihao Chen, Abdul Ali Bangash et al.

In machine learning (ML), efficient asset management, including ML models, datasets, algorithms, and tools, is vital for resource optimization, consistent performance, and a streamlined development lifecycle. This enables quicker iterations, adaptability, reduced development-to-deployment time, and reliable outputs. Despite existing research, a significant knowledge gap remains in operational challenges like model versioning, data traceability, and collaboration, which are crucial for the success of ML projects. Our study aims to address this gap by analyzing 15,065 posts from developer forums and platforms, employing a mixed-method approach to classify inquiries, extract challenges using BERTopic, and identify solutions through open card sorting and BERTopic clustering. We uncover 133 topics related to asset management challenges, grouped into 16 macro-topics, with software dependency, model deployment, and model training being the most discussed. We also find 79 solution topics, categorized under 18 macro-topics, highlighting software dependency, feature development, and file management as key solutions. This research underscores the need for further exploration of identified pain points and the importance of collaborative efforts across academia, industry, and the research community.

Syeda Kaneez Fatima, Yousuf Abrar, Abdul Rehman Tahir et al.

Autonomous coding agents are reshaping software development by creating pull requests (PRs) on GitHub, referred to as agentic PRs. In parallel, the review process is also becoming autonomous, thereby making reviewer bots key actors in the assessment of these agentic PRs. However, their influence on PR acceptance and resolution remains unclear. This study empirically investigates the relationship between reviewer-bot feedback and PR outcomes by analyzing how Reviewer Bot Feedback Quality (relevance, clarity, conciseness) and Reviewer Bot Activity Volume (comment count) are associated with PR acceptance and resolution time. We analyze 7,416 reviewer-bot comments on 4,532 PRs from the AI_Dev dataset (a dataset that captured AI agents' PRs in GitHub projects). Our results show that reviewer-bot comments mainly focus on bug fixes, testing, and documentation, are civil in tone, and are prescriptive in nature. Reviewer bots generally produce clear and concise feedback, though the semantic relevance of comments to underlying code changes is moderate. We find that higher Reviewer Bot Activity volume is associated with longer PR resolution times and lower average feedback quality, showing that as bots generate more comments on a PR, the average pertinence of that feedback appears to degrade. At the same time, Reviewer Bot Feedback Quality shows no meaningful association with workflow outcomes. Our findings suggest that, in agentic PR workflows, reviewer bots should prioritize targeted high-relevance feedback over generating large numbers of comments.

Ernesto Lang Oreamuno, Rohan Faiyaz Khan, Abdul Ali Bangash et al.

Model stores offer third-party ML models and datasets for easy project integration, minimizing coding efforts. One might hope to find detailed specifications of these models and datasets in the documentation, leveraging documentation standards such as model and dataset cards. In this study, we use statistical analysis and hybrid card sorting to assess the state of the practice of documenting model cards and dataset cards in one of the largest model stores in use today--Hugging Face (HF). Our findings show that only 21,902 models (39.62\%) and 1,925 datasets (28.48\%) have documentation. Furthermore, we observe inconsistency in ethics and transparency-related documentation for ML models and datasets.

Abdul Ali Bangash, Hareem Sahar, Abram Hindle et al.

Researchers in empirical software engineering often make claims based on observable data such as defect reports. Unfortunately, in many cases, these claims are generalized beyond the data sets that have been evaluated. Will the researcher's conclusions hold a year from now for the same software projects? Perhaps not. Recent studies show that in the area of Software Analytics, conclusions over different data sets are usually inconsistent. In this article, we empirically investigate whether conclusions in the area of defect prediction truly exhibit stability throughout time or not. Our investigation applies a time-aware evaluation approach where models are trained only on the past, and evaluations are executed only on the future. Through this time-aware evaluation, we show that depending on which time period we evaluate defect predictors, their performance, in terms of F-Score, the area under the curve (AUC), and Mathews Correlation Coefficient (MCC), varies and their results are not consistent. The next release of a product, which is significantly different from its prior release, may drastically change defect prediction performance. Therefore, without knowing about the conclusion stability, empirical software engineering researchers should limit their claims of performance within the contexts of evaluation, because broad claims about defect prediction performance might be contradicted by the next upcoming release of a product under analysis.