Related references
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Article
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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2023)
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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.
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JOURNAL OF SYSTEMS AND SOFTWARE
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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.
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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
Article
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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.
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Article
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Article
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EMPIRICAL SOFTWARE ENGINEERING
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Article
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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
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Article
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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
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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)
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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)
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Guoliang Zhao et al.
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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
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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.
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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.
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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2021)
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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.
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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
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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
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