A Fault Prediction Approach based on the Probabilistic Model for Improvising Software Inspection

Abstract

Objective: Software development is a multitask activity performed by a team. Each activity involves with different tasks and complexity. To achieve quality of improvement it is important that each activity task should be fault free. But, finding and correcting faults are most expensive and time consuming. Methods: Software inspection is a static analysis technique which does not required program execution, instead it use inspector to make decision during the development. Findings: But it was observed in literature that inspection has bad records in finding accurate defects in software development. In this paper, we present a novel Fault Prediction Approach (FPA) based on the probabilistic model to improvise the software inspection to detect the defect accurately and cost effective for the quality software development. Application/ Improvement: Inspection is an effective activity to find the defects using empirical data in the initial stage of development. The proposed FPA investigate a probabilistic methods using modified Naive Bayes classification to estimate the probable faults in an experiment context to suggest fault controlling development. Further, the analysis investigates how FPA effectively identifying the faults during the inspection and impact in the quality development performance.