Important issues in software fault prediction: a road map

Golnoush Abaei, Ali Selamat

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Otherpeer-review

Abstract

Quality assurance tasks such as testing, verification and validation, fault tolerance, and fault prediction play a major role in software engineering activities. Fault prediction approaches are used when a software company needs to deliver a finished product while it has limited time and budget for testing it. In such cases, identifying and testing parts of the system that are more defect prone is reasonable. In fact, prediction models are mainly used for improving software quality and exploiting available resources. Software fault prediction is studied in this chapter based on different criteria that matters in this research field. Usually, there are certain issues that need to be taken care of such as different machine-learning techniques, artificial intelligence classifiers, variety of software metrics, distinctive performance evaluation metrics, and some statistical analysis. In this chapter, the authors present a roadmap for those researchers who are interested in working in this area. They illustrate problems along with objectives related to each mentioned criterion, which could assist researchers to build the finest software fault prediction model.

Original languageEnglish
Title of host publicationComputer Systems and Software Engineering
Subtitle of host publicationConcepts, Methodologies, Tools, and Applications
PublisherIGI Global
Pages162-190
Number of pages29
ISBN (Electronic)9781522539247
ISBN (Print)9781522539230
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes

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