SLMBC: spiral life cycle model-based Bayesian classification technique for efficient software fault prediction and classification

• Published in: Soft computing (Berlin, Germany) Vol. 21; no. 2; pp. 403 - 415
• Main Authors: Chinna Gounder Dhanajayan, Rajaganapathy, Appavu Pillai, Subramani
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2017; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Bayesian analysis; Classification; Clustering; Computational Intelligence; Control; Data mining; Datasets; Decision trees; Defects; Engineering; Error analysis; Failure; Fault diagnosis; Fuzzy logic; Growth models; Integrated software; Machine learning; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Methods; Modules; Neural networks; Product development; Quality assurance; Robotics; Similarity; Similarity measures; Software development; Software quality; Software reliability; Spiral life cycle model-based Bayesian classification technique (SLMBC); Segregate fault prediction algorithm; Spiral life cycle model; Bayesian classification; Software development; Robust similarity-aware clustering (RSC) algorithm
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-016-2316-6

Software fault prediction and classification plays a vital role in the software development process for assuring high quality and reliability of the software product. Earlier prediction of the fault-prone software modules enables timely correction of the faults and delivery of reliable product. Generally, the fuzzy logic, decision tree and neural networks are deployed for fault prediction. But these techniques suffer due to low accuracy and inconsistency. To overcome these issues, this paper proposes a spiral life cycle model-based Bayesian classification technique for efficient software fault prediction and classification. In this process, initially the dependent and independent software modules are identified. The spiral life cycle model is used for testing the software modules in each life cycle of the software development process. Bayesian classification is applied to classify the software modules as faulty module and non-faulty module, by using the probability distribution models. Robust similarity-aware clustering algorithm performs clustering of the faulty and non-faulty software modules based on the similarity measure of the features in the dataset. From the experimental results, it is observed that the proposed method enables accurate prediction and classification of the faulty modules. The proposed technique achieves higher accuracy, precision, recall, probability of detection, F-measure and lower error rate than the existing techniques. The misclassification rate of the proposed technique is found to be lower than the existing techniques. Hence, the reliability of the software development process can be improved.