Related references
Note: Only part of the references are listed.
Article
Computer Science, Software Engineering
Jiaxuan Han et al.
Summary: Bug Localization technologies have been widely used, but current methods only consider source code information at the text level. This paper presents an improved bug localization model that utilizes graph-level semantics to supplement text-level semantics, and uses attention mechanism to optimize and adjust the graph semantics. The model achieves better performance than state-of-the-art methods according to experiments on three open source Java projects.
AUTOMATED SOFTWARE ENGINEERING
(2023)
Article
Computer Science, Software Engineering
Emily Winter et al.
Summary: Automatic program repair (APR) is a rapidly advancing field in software engineering. This article fills the gap in research by reporting on a survey of 386 software developers about their bug finding and fixing practices and their attitudes towards APR. The findings show that bug finding and fixing is not as burdensome as previously thought and that developers derive satisfaction from it. Developers prefer to be involved in the APR process rather than having it fully automated, indicating the importance of considering developer agency and presenting information alongside fixes.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2023)
Article
Computer Science, Software Engineering
Hideaki Azuma et al.
Summary: This paper focuses on the self-admitted technical debt (SATD) in Dockerfiles and categorizes them through manual classification. The study found that about 3.0% of the comments in Dockerfiles are SATD. In addition, SATDs were classified into five classes and eleven subclasses, with some specific to Docker.
EMPIRICAL SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Software Engineering
Thazin Win Win Aung et al.
Summary: This study introduces a multi-triage model that addresses the tasks of assigning developers and allocating issue types simultaneously through multi-task learning. The model utilizes a text encoder and AST encoder to extract feature representations, with a data augmentation approach introduced to balance class labels. Experiments demonstrate the effectiveness of the model compared to state-of-the-art methods on eleven open-source projects.
JOURNAL OF SYSTEMS AND SOFTWARE
(2022)
Article
Computer Science, Software Engineering
Md Nadim et al.
Summary: This article introduces a method for predicting just-in-time bugs in software systems using data visualization. The author converts source code into graph representations and uses the attributes of these graphs to visualize and detect buggy software commits. The investigation on 12 open-source software projects shows that combining graph features with conventional features can improve the performance of machine learning-based buggy commit detection models.
AUTOMATED SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Software Engineering
Ankur Tagra et al.
Summary: Reopened bugs can degrade software quality and trust from end-users. This study revisits reopened bugs and provides new insights into bug reopening activities.
EMPIRICAL SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Software Engineering
Gema Rodriguez-Perez et al.
Summary: This paper investigates the impact of extrinsic bugs on software engineering research. By reanalyzing previous studies, we find that extrinsic bugs are of a different nature compared to intrinsic bugs, and they have a negative impact on just-in-time models.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Information Systems
Saurabh Malgaonkar et al.
Summary: The study developed two novel automated prioritization techniques to rank informative app reviews, using time and accuracy metrics to measure their performance. Results showed promising outcomes in terms of time and accuracy, indicating potential support for app developers in identifying and prioritizing informative reviews.
INFORMATION AND SOFTWARE TECHNOLOGY
(2022)
Article
Computer Science, Software Engineering
Junjie Wang et al.
Summary: Identifying and optimizing open participation is crucial for successful open software development. This article discusses the limitations of existing worker recommendation studies for crowdtesting tasks and proposes a context- and fairness-aware in-process crowdworker recommendation approach, called iRec2.0. Evaluation on 636 crowdtesting tasks shows that iRec2.0 has the potential to improve cost-effectiveness, shorten testing process, and alleviate unfairness among workers.
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
(2022)
Article
Computer Science, Software Engineering
Steffen Herbold et al.
Summary: This article examines the prevalence and impact of tangled commits in software bug fixes. Through a crowdsourcing approach, the researchers found that some commits addressing bug fixes are tangled and may introduce noise. The authors warn researchers to validate data before using it.
EMPIRICAL SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Software Engineering
Biruk Asmare Muse et al.
Summary: Developers may take shortcuts in design and implementation due to tight release schedules, leading to technical debt. SATD in data-intensive systems can seriously impact performance and maintainability. Through empirical studies and manual analysis, managing and understanding technical debt can be done more efficiently.
EMPIRICAL SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Software Engineering
Nathan Cassee et al.
Summary: This paper studies the relationship between different types of Self-Admitted Technical Debt (SATD) comments in source code and their polarity, aiming to understand the circumstances and reasons why developers use negative or neutral comments to highlight SATD. A manual analysis and survey involving professional developers are conducted to categorize SATD content, examine the expression of negative sentiment in different types of SATD, and investigate developers' opinions on using negative sentiment as a proxy for priority. Results show that SATD comments are mainly used for annotating poor implementation choices and partially implemented functionality. Around 30% of the respondents agree on using negative sentiment as a proxy for priority, while half of them suggest discussing SATD in issue trackers rather than in the source code.
EMPIRICAL SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Software Engineering
An Ran Chen et al.
Summary: Researchers proposed an IRBL approach Pathidea that leverages log information in bug reports to reconstruct execution paths, achieving significant improvements in bug localization results. Experiments on multiple open source systems demonstrated Pathidea's effectiveness in enhancing retrieval accuracy and ranking precision, providing valuable assistance for developers in debugging and analysis.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Information Systems
Xiaoxue Ma et al.
Summary: Inappropriate public disclosure of security bug reports (SBRs) can attract malicious attackers to invade software systems, making it crucial to detect SBRs for software maintenance. However, existing techniques for identifying SBRs still face challenges such as class imbalance, insufficient training information, and weak performance robustness. This study proposes the CASMS approach to improve performance and robustness by addressing the class imbalance problem and extracting more semantic information.
INFORMATION AND SOFTWARE TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Xiuting Ge et al.
Summary: This research proposes a locality-based active learning approach for identifying security bug reports, which improves the performance and efficiency of SBR identification by utilizing local features in the training set and query process.
INFORMATION AND SOFTWARE TECHNOLOGY
(2022)
Article
Computer Science, Software Engineering
Rishab Sharma et al.
Summary: Self-Admitted Technical Debt (SATD) in scientific software, particularly in R packages, poses a challenge for automated detection. This study investigates the use of classification models, including Pre-Trained Language Models (PTMs), to detect SATD types in R packages. The results show that PTMs, such as RoBERTa, outperform other models and are effective even when SATD occurrences are low. Additionally, the study identifies several causes of SATD in scientific software, including some previously unknown causes.
AUTOMATED SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Software Engineering
Tao Xiao et al.
Summary: This paper presents a qualitative analysis of 500 self-admitted technical debt (SATD) comments in the Maven build system to characterize SATD by location and rationale. The study shows that limitations in tools and libraries, and complexities of dependency management are the most frequent causes of SATD. The authors also train classifiers to automatically detect the reasons and purposes of SATD, achieving a promising performance. The work highlights the importance of understanding technical debt in build systems and suggests future directions for tool support.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Software Engineering
Guoliang Zhao et al.
Summary: High performance is crucial for software system success. Performance anomalies, if not detected early, can have a negative impact on user satisfaction. Existing detection methods have limitations in predicting anomalies in advance, causing delays in corrective actions.
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
(2021)
Article
Computer Science, Software Engineering
Sophia Quach et al.
Summary: Non-functional bugs, such as performance bugs and security bugs, incur high costs for both software developers and end-users, with IBM estimating millions of dollars for a single data breach. The commonly used SZZ approach for identifying bug-inducing changes may not be optimal for non-functional bugs, as shown in empirical studies. Manual examination reveals that a large portion of non-functional bugs cannot be properly identified by the SZZ approach, highlighting the need to complement SZZ for unique characteristics of non-functional bugs.
EMPIRICAL SOFTWARE ENGINEERING
(2021)
Article
Computer Science, Information Systems
Xiaoxue Wu et al.
Summary: The study introduces hbrPredictor, a method that combines interactive machine learning and active learning for HBR prediction. It significantly reduces the number of bug reports needed for training the prediction model while enhancing the diversity and generalization ability of training samples through uncertainty sampling. Experimental results show that hbrPredictor outperforms baselines in security bug report prediction, achieving maximum values of F1-score (0.7939) and AUC (0.8789), and can reach optimal performance with a significantly reduced number of bug reports for both small-sized and large-sized datasets.
INFORMATION AND SOFTWARE TECHNOLOGY
(2021)
Article
Computer Science, Software Engineering
Zhaoqiang Guo et al.
Summary: This study investigates the identification of self-admitted technical debt (SATD) by comparing existing approaches with a simple approach that leverages predefined task tags. The results show that the simple approach has similar or even superior performance in SATD identification compared to existing methods, suggesting it should be used as a baseline in future research.
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
(2021)
Article
Computer Science, Software Engineering
Yuanrui Fan et al.
Summary: This study investigated the impact of mislabeled changes by different SZZ variants on JIT defect prediction models. The results show that B-SZZ and MA-SZZ mislabeled changes are less likely to cause a significant performance reduction, while AG-SZZ mislabeled changes may lead to significant performance reduction.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2021)
Article
Computer Science, Software Engineering
Vahid Etemadi et al.
Summary: This paper introduces a strategy to minimize the time and cost of bug fixing tasks by using a scheduling-driven approach, which proves to be superior in efficiency and performance through empirical studies and comparisons with state-of-the-art solutions.
JOURNAL OF SYSTEMS AND SOFTWARE
(2021)
Article
Computer Science, Software Engineering
Xiaoxue Wu et al.
Summary: This study reveals the presence of mislabeled instances in datasets for security bug report prediction, which has negatively impacted the performance of previous models. However, after cleaning the datasets, the performance of classification models has significantly improved.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2021)
Article
Computer Science, Software Engineering
Mikolaj Fejzer et al.
Summary: The novel adaptive bug localization algorithm is based on new feature weighting approaches and utilizes pointwise learn-to-rank method for adaptive selection. It is competitive in accuracy and required computational resources compared to state-of-the-art technologies.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2021)
Article
Computer Science, Software Engineering
Lin Chen et al.
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
(2020)
Article
Computer Science, Software Engineering
Chuanqi Wang et al.
JOURNAL OF SYSTEMS AND SOFTWARE
(2020)
Article
Computer Science, Software Engineering
Harold Valdivia-Garcia et al.
JOURNAL OF SYSTEMS AND SOFTWARE
(2018)
Article
Computer Science, Software Engineering
Zeeshan Ahmed Nizamani et al.
AUTOMATED SOFTWARE ENGINEERING
(2018)
Proceedings Paper
Computer Science, Software Engineering
Yibiao Yang et al.
FSE'16: PROCEEDINGS OF THE 2016 24TH ACM SIGSOFT INTERNATIONAL SYMPOSIUM ON FOUNDATIONS OF SOFTWARE ENGINEERING
(2016)
Article
Computer Science, Information Systems
Xin Xia et al.
INFORMATION AND SOFTWARE TECHNOLOGY
(2015)
Article
Computer Science, Software Engineering
Yuming Zhou et al.
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
(2014)
Review
Reproductive Biology
P. F. Watson et al.
