Software Bug Reports: Automatic Keyword and Sentence-Based Text Summarization Using Artificial Intelligence (International Journal of Innovative Research in Computer Science & Technology, 2022)
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.
Keywords: Rapid Automatic Keyword Extraction; Text Summarization; Fuzzy C-Means; Bug Reports; Hierarchical Clustering; Rule Engine