arXiv subjects

Md Nahidul Islam Opu

Publications and source records attributed to Md Nahidul Islam Opu.

9 recordsLinked to original sources

arXiv2026

A First Look at the Self-Admitted Technical Debt in Test Code: Taxonomy and Detection

Self-admitted technical debt (SATD) refers to comments in which developers explicitly acknowledge code issues, workarounds, or suboptimal solutions. SATD is known to significantly increase software maintenance effort. While extensive research has examined SATD in source code, its presence and impact in test code have received no focused attention, leaving a significant gap in our understanding of how SATD manifests in testing contexts. This study investigates SATD in test code by manually analyzing 50,000 comments randomly sampled from 1.6 million comments across 1,000 open-source Java projects. From this sample, after manual analysis and filtering, we identified 615 SATD comments and classified them into 11 distinct categories, building a taxonomy of test code SATD. To investigate whether test code SATD can be detected automatically, we evaluated existing SATD detection tools, as well as both open-source and proprietary LLMs. Among the existing tools, MAT performed the best, albeit with moderate recall. To our surprise, both open-source and proprietary LLMs exhibited poor detection accuracy. These results indicate that neither existing approaches nor current LLMs can reliably detect SATD in test code. Overall, this work provides the results of a large-scale analysis of SATD in test code, a nuanced understanding of its types, and the limitations of current SATD detection methods. Our findings lay the groundwork for future research on test code-specific SATD.

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arXiv2026

Robotics Meets Software Engineering: A First Look at the Robotics Discussions on Stackoverflow

Robots can greatly enhance human capabilities, yet their development presents a range of challenges. This collaborative study, conducted by a team of software engineering and robotics researchers, seeks to identify the challenges encountered by robot developers by analyzing questions posted on StackOverflow. We created a filtered dataset of 500 robotics-related questions and examined their characteristics, comparing them with randomly selected questions from the platform. Our findings indicate that the small size of the robotics community limits the visibility of these questions, resulting in fewer responses. While the number of robotics questions has been steadily increasing, they remain less popular than the average question and answer on StackOverflow. This underscores the importance of research that focuses on the challenges faced by robotics practitioners. Consequently, we conducted a thematic analysis of the 500 robotics questions to uncover common inquiry patterns. We identified 11 major themes, with questions about robot movement being the most frequent. Our analysis of yearly trends revealed that certain themes, such as Specifications, were prominent from 2009 to 2014 but have since diminished in relevance. In contrast, themes like Moving, Actuator, and Remote have consistently dominated discussions over the years. These findings suggest that challenges in robotics may vary over time. Notably, the majority of robotics questions are framed as How questions, rather than Why or What questions, revealing the lack of enough resources for the practitioners. These insights can help guide researchers and educators in developing effective and timely educational materials for robotics practitioners.

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arXiv2026

The Repeat Offenders: Characterizing and Predicting Extremely Bug-Prone Source Methods

Bug prediction has long been considered the "prince" of empirical software engineering research, and accordingly, a substantial body of work has focused on predicting bugs to enable early preventive actions. However, most existing studies operate at the class or file level, which practitioners have found to be of limited practical value. As a result, method-level bug prediction has gained increasing attention in recent years. Despite this shift, current method-level prediction models typically treat all buggy methods as equally fault-prone, regardless of whether a method has been associated with a bug once or repeatedly. We argue that methods involved in bugs multiple times-hereafter referred to as ExtremelyBuggy methods-are more harmful than methods that are buggy only once. In this study, we investigate the prevalence of ExtremelyBuggy methods, analyze their code quality metrics, and assess whether they can be predicted at the time of their introduction. In addition, we conduct a thematic analysis of 287 ExtremelyBuggy methods to gain deeper insights into their characteristics. Using a dataset of over 1.25 million methods extracted from 98 open-source Java projects, we find that only a small proportion of methods can be classified as ExtremelyBuggy, yet these methods account for a disproportionately large share of bugs within a project. Although we observe statistically significant differences between ExtremelyBuggy and other methods, ExtremelyBuggy methods remain difficult to predict at their inception. Nevertheless, our manual analysis reveals recurring characteristics among these methods. These findings can help practitioners avoid harmful patterns in practice and guide future research toward developing features and models that better capture the unique properties of such methods.

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arXiv2025

How Do Agentic AI Systems Address Performance Optimizations? A BERTopic-Based Analysis of Pull Requests

LLM-based software engineering is influencing modern software development. In addition to correctness, prior studies have also examined the performance of software artifacts generated by AI agents. However, it is unclear how exactly the agentic AI systems address performance concerns in practice. In this paper, we present an empirical study of performance-related pull requests generated by AI agents. Using LLM-assisted detection and BERTopic-based topic modeling, we identified 52 performance-related topics grouped into 10 higher-level categories. Our results show that AI agents apply performance optimizations across diverse layers of the software stack and that the type of optimization significantly affects pull request acceptance rates and review times. We also found that performance optimization by AI agents primarily occurs during the development phase, with less focus on the maintenance phase. Our findings provide empirical evidence that can support the evaluation and improvement of agentic AI systems with respect to their performance optimization behaviors and review outcomes.

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arXiv2025

How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Study

As Software Engineering enters its new era (SE 3.0), AI coding agents increasingly automate software development workflows. However, it remains unclear how exactly these agents recognize and address software energy concerns-an issue growing in importance due to large-scale data centers, energy-hungry language models, and battery-constrained devices. In this paper, we examined the energy awareness of agent-authored pull requests (PRs) using a publicly available dataset. We identified 216 energy-explicit PRs and conducted a thematic analysis, deriving a taxonomy of energy-aware work. Our further analysis of the applied optimization techniques shows that most align with established research recommendations. Although building and running these agents is highly energy intensive, encouragingly, the results indicate that they exhibit energy awareness when generating software artifacts. However, optimization-related PRs are accepted less frequently than others, largely due to their negative impact on maintainability.

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arXiv2025

LLM-Based Detection of Tangled Code Changes for Higher-Quality Method-Level Bug Datasets

Tangled code changes, commits that conflate unrelated modifications such as bug fixes, refactorings, and enhancements, introduce significant noise into bug datasets and adversely affect the performance of bug prediction models. Addressing this issue at a fine-grained, method-level granularity remains unexplored. This is critical to address, as recent bug prediction models, driven by practitioner demand, are increasingly focusing on finer granularity rather than traditional class- or file-level predictions. This study investigates the utility of Large Language Models (LLMs) for detecting tangled code changes by leveraging both commit messages and method-level code diffs. We formulate the problem as a binary classification task and evaluate multiple prompting strategies, including zero-shot, few-shot, and chain-of-thought prompting, using state-of-the-art proprietary LLMs such as GPT-5 and Gemini-2.0-Flash, and open-source models such as GPT-OSS-120B and CodeBERT. Our results demonstrate that combining commit messages with code diffs significantly enhances model performance, with the combined few-shot and chain-of-thought prompting achieving an F1-score of 0.883. Additionally, we explore machine learning models trained on LLM-generated embeddings, where a multi-layer perceptron classifier achieves superior performance (F1-score: 0.906, MCC: 0.807). Applying our approach to 49 open-source projects improves the distributional separability of code metrics between buggy and non-buggy methods, demonstrating the promise of LLMs for method-level commit untangling and potentially contributing to improving the accuracy of future bug prediction models.

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arXiv2025

Understanding the Issue Types in Open Source Blockchain-based Software Projects with the Transformer-based BERTopic

Blockchain-based software systems are increasingly deployed across diverse domains, yet a systematic understanding of their development challenges remains limited. This paper presents a large-scale empirical study of 497,742 issues mined from 1,209 open-source blockchain projects hosted on GitHub. Employing BERTopic, a transformer-based topic modeling technique, we identify 49 distinct issue topics and organize them hierarchically into 11 major subcategories. Our analysis reveals that both general software development issues and blockchain-specific concerns are nearly equally represented, with Wallet Management and UI Enhancement emerging as the most prominent topics. We further examine the temporal evolution of issue categories and resolution times, finding that Wallet issues not only dominate in frequency but also exhibit the longest resolution time. Conversely, Mechanisms issues are resolved significantly faster. Issue frequency surged after 2016 with the rise of Ethereum and decentralized applications, but started declining after 2022. These findings enhance our understanding of blockchain software maintenance, informing the development of specialized tools and practices to improve robustness and maintainability.

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arXiv2025

Moderately Mighty: To What Extent Can Internal Software Metrics Predict App Popularity at Launch?

Predicting a mobile app's popularity before its first release can provide developers with a strategic advantage in a competitive marketplace, yet it remains a challenging problem. This study explores the extent to which internal software metrics, measurable from source code before deployment, can predict an app's popularity (i.e., ratings and downloads per year) at inception. For our analysis, we constructed a rigorously filtered dataset of 446 open-source Java-based Android apps that are available on both F-Droid and Google Play Store. Using app source code from F-Droid, we extracted a wide array of internal metrics, including system-, class-, and method-level code metrics, code smells, and app metadata. Popularity-related information, including reviews and download counts, was collected from the Play Store. We evaluate regression and classification models across three feature sets: a minimal Size-only baseline, a domain-informed Handpicked set, and a Voting set derived via feature selection algorithms. Our results show that, for both app ratings and number of downloads, regression models perform poorly due to skewed rating distributions and a highly scattered range of download counts in our dataset. However, when reframed as a binary classification (Popular vs. Unpopular), performance improves significantly-the best model, a Multilayer Perceptron, achieves an F1-score of 0.72. We conclude that, although internal code metrics alone are insufficient for accurately predicting an app's future popularity, they do exhibit meaningful correlations with it. Thus, our findings challenge prior studies that have entirely dismissed internal metrics as valid indicators of software quality. Instead, our results align with research suggesting that internal code metrics can be valuable when evaluated within the appropriate context-specifically, we found them useful for classification tasks.

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arXiv2025

Exploring Challenges in Test Mocking: Developer Questions and Insights from StackOverflow

Mocking is a common unit testing technique that is used to simplify tests, reduce flakiness, and improve coverage by replacing real dependencies with simplified implementations. Despite its widespread use in Open Source Software projects, there is limited understanding of how and why developers use mocks and the challenges they face. In this collaborative study, we have analyzed 25,302 questions related to Mocking on STACKOVERFLOW to identify the challenges faced by developers. We have used Latent Dirichlet Allocation for topic modeling, identified 30 key topics, and grouped the topics into five key categories. Consequently, we analyzed the annual and relative probabilities of each category to understand the evolution of mocking-related discussions. Trend analysis reveals that category like Advanced Programming peaked between 2009 and 2012 but have since declined, while categories such as Mocking Techniques and External Services have remained consistently dominant, highlighting evolving developer priorities and ongoing technical challenges. Our findings also show an inverse relationship between a topic's popularity and its difficulty. Popular topics like Framework Selection tend to have lower difficulty and faster resolution times, while complex topics like HTTP Requests and Responses are more likely to remain unanswered and take longer to resolve. A classification of questions into How, Why, What, and Other revealed that over 70% are How questions, particularly in practical domains like file access and APIs, indicating a strong need for implementation guidance. Why questions are more prevalent in error-handling contexts, reflecting conceptual challenges in debugging, while What questions are rare and mostly tied to theoretical discussions. These insights offer valuable guidance for improving developer support, tooling, and educational content in the context of mocking and unit testing.

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