Software Bug Reports: Automatic Keyword and Sentence-Based  Text Summarization Using Artificial Intelligence

Zaid Altaf; Ashish Oberoi

doi:10.55524/

Authors

Zaid Altaf M. Tech Scholar, Department of Computer Science & Engineering, RIMT University, Mandi Gobindgarh, Punjab, India Author
Ashish Oberoi Assistant Professor, Department of Computer Science & Engineering, RIMT University, Mandi Gobindgarh, Punjab, India Author

DOI:

https://doi.org/10.55524/

Keywords:

Rapid Automatic Keyword Extraction, Text Summarization, Fuzzy C-Means, Bug Reports, Hierarchical Clustering, Rule Engine

Abstract

The purpose of text summarization is to quickly and accurately extract the most important data from papers. The proposed unsupervised method seeks to synthesise complete and informative bug reports (software artefacts). The suggested approach employs Rapid Auto

matic Keyword Extraction and the term frequency-inverse document frequency method to identify applicable keywords and phrases. During the sentence extraction procedure, fuzzy C-means clustering is used to prioritise sentences that have a high degree of membership in each cluster (beyond a predefined threshold). The selection of sentences is performed by a rule-engine. Information is extracted using keywords and sentences chosen by the clustering process, and the rules are developed using domain knowledge. The proposed method produces a logical and well-organized summary of apache bug reports. The retrieval summary is improved with the help of hierarchical clustering by removing unnecessary details and rearranging them. The Apache Project Bug Report Corpus (APBRC) and the original Bug Report Corpus are used to evaluate the effectiveness of the proposed method. Measures of performance such as precision, recall, pyramid precision, and F-score are used to evaluate the results. Experiment results demonstrate that our proposed method significantly outperforms the state-of-the-art baseline methods like BRC and LRCA. In addition, it achieves substantial gains compared to prior art unsupervised methods as Hurried and centroid. It extracts the most relevant keyword phrases and sentences from each cluster to offer comprehensive coverage and a coherent summary. The average values for precision, recall, f-score, and pyramid precision on the APBRC corpus are 78.22%, 82.18%, 80.10%, and 81.66%, respectively.

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