An approach to generate the bug report summaries using two-level feature extraction

Gupta, Som; Gupta, Shilpi

doi:10.1016/j.eswa.2021.114816

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…The used state acronyms are defined in Table 3. As compared with the presented profiles, PL P (P1) and PL P (MongoDB), PL P (Log4J2) comprises a lower number of paths (99) with a lower number of states (2)(3)(4)(5)(6)(7)(8)(9). Two-and three-state paths are dominant (54.2% and 32.5% issue coverage).…”

Section: )mentioning

confidence: 85%

“…Data collected in software repositories are the basis for diverse practical and research studies in the software engineering domain. Here, we can distinguish the following aspects considered in the literature on software repositories: the detection of duplicate issues [4,5], bug prediction and issue classification [6,7], bug triaging [8], and issue report textual analysis [9,10]. Most papers related to software bugs focus on a single selected problem considering coarse-grained data from repositories.…”

Section: Introductionmentioning

confidence: 99%

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Graph-Driven Exploration of Issue Handling Schemes in Software Projects

Dobrzyński,

Sosnowski

2024

Applied Sciences

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The Issue Tracking System (ITS) repositories are rich sources of software development documentation that are useful in assessing the status and quality of software projects. An original model is proposed for tracing issue handling activities and their impact on project progress. As opposed to classical data mining of software repositories, we consider fine-grained features of issues which provide a better insight into project evolution. A thorough analysis of repository contents allows us to define useful metrics for characterizing issue handling schemes. These metrics are derived from the introduced graph model and developed original data mining algorithms targeting timing, issue flow progress and project actor activity aspects. This study is associated with issue processing states and their sequences (handling paths), leading to problem resolution. The introduced taxonomy of issue processing schemes facilitates the creation of a pertinent knowledge database and the identification of both bad (anomalies) and good practices. The proposed approach is illustrated with experimental results related to a representative set of ITS project repositories. These results enhance experts’ knowledge of the project and can be used for correct decision-making actions. They reveal weak points in project development and possible directions for improvement.

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Section: )mentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Graph-Driven Exploration of Issue Handling Schemes in Software Projects

Dobrzyński,

Sosnowski

2024

Applied Sciences

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“…Then in the left thigh, a groin incision was made and the greater saphenous vein was dissected out in the same way as was on the other side Several minutes of compression was used for haemostasis generated generic summary is shown in Table 8 in the implementation section. The score value is given up-to-the scale value to (Fleuren & Alkema, 2015;Gupta & Gupta, 2021;Jindal & Kaur, 2020;Krasniqi, 2021;Mishra et al, 2014;Nath & Roy, 2022;Rambow et al, 2004;Wan & McKeown, 2004;Zechner, 2002a;Zhou et al, 2003a) and 10 is the maximum summary score for each summary. Table 12 and Figure 6 show the score specified by these experts to the generated generic summary in biomedical text data.…”

Section: Rake Sentences Concept Map Sentencesmentioning

confidence: 99%

“…To mitigate this issue, automatic text summarization plays a crucial role in acquiring potent knowledge effectively and efficiently from the vast domain of information. It has been extensively applied on several domains such as document summarization (Zechner, 2002a; Zhou et al, 2003a), e‐mail summarization (Rambow et al, 2004; Wan & McKeown, 2004) and bug report summarization (Gupta & Gupta, 2021; Jindal & Kaur, 2020; Krasniqi, 2021; Nath & Roy, 2022). Also, there is a huge increase in electronic content of textual data in biomedical and health‐care domains in scientific articles, medical guidelines, clinical trial reports (Fleuren & Alkema, 2015; Mishra et al, 2014) and health records (Moradi & Ghadiri, 2018).…”

Section: Introductionmentioning

confidence: 99%

Extractive summarization using concept‐space and keyword phrase

2022

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PROBLEM: Text summarization is widely used to extract relevant information from documents. The extensive unstructured data are generated in various domains, developing a concise and coherent form of information has become an inevitable and assertive problem among the research community. Automatic text summarization plays a crucial role in acquiring potent knowledge effectively and efficiently for each domain of information. Medical data contains crucial information regarding human diseases and their symptoms. The proposed method will benefit the research community and health experts as it saves a lot of time and resources to compute the patients' summary during diagnosis. APPROACH: In this research, an unsupervised approach is proposed for extractive summarization, based on semantic similarity and keyword-phrase extraction. Merging Concept Map and the RAKE method, a generic summary is computed based on threshold values. Both single-document and multi-document summarization can be accomplished with the approach. RESULTS: To evaluate the proposed unsupervised approach, various biomedical transcripts of neuro-science, general medicine, gastroenterology, orthopaedic and radiology domain are used. MT Sample Dataset is used to collect 1040 different transcripts. Using the proposed approach, an average ROUGE score of 0.77 for single-document summarization; however, for generic summary, an average ROUGE is 0.72. The proposed technique is validated for the previous corpus of BioMed articles, and results are better with state-of-the-art techniques.

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“…At the same time, the final hidden-layer state, obtained from the unidirectional LSTM at the decoder, is regarded as a semantic vector of the generated text summary. The semantic similarity can be expressed by calculating the cosine similarity [28] of and . As the source text and generated text summary are located in the same semantic space, the cosine similarity can effectively measure the distance between them.…”

Section: Figure 4 the Proposed Esn Modelmentioning

confidence: 99%

Enhancements of Attention-Based Bidirectional LSTM for Hybrid Automatic Text Summarization

et al. 2021

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The automatic generation of a text summary is a task of generating a short summary for a relatively long text document by capturing its key information. In the past, supervised statistical machine learning was widely used for this Automatic Text Summarization (ATS) task, but due to its high dependence on the quality of text features, the generated summaries lack accuracy and coherence, while the computational power involved, and performance achieved, could not easily meet the current needs. This paper proposes four novel ATS models with a Sequence-to-Sequence (Seq2Seq) structure, utilizing an attention-based bidirectional Long Short-Term Memory (LSTM), with added enhancements for increasing the correlation between the generated text summary and the source text, and solving the problem of unregistered words, suppressing the repeated words, and preventing the spread of cumulative errors in generated text summaries. Experiments conducted on two public data sets confirmed that the proposed ATS models achieve indeed better performance than the baselines and some of the state-of-the-art models considered.

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An approach to generate the bug report summaries using two-level feature extraction

Cited by 9 publications

References 17 publications

Graph-Driven Exploration of Issue Handling Schemes in Software Projects

Graph-Driven Exploration of Issue Handling Schemes in Software Projects

Extractive summarization using concept‐space and keyword phrase

Enhancements of Attention-Based Bidirectional LSTM for Hybrid Automatic Text Summarization

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