A defect prediction method for software versioning

Abstract

New methodologies and tools have gradually made the life cycle for software development more human-independent. Much of the research in this field focuses on defect reduction, defect identification and defect prediction. Defect prediction is a relatively new research area that involves using various methods from artificial intelligence to data mining. Identifying and locating defects in software projects is a difficult task. Measuring software in a continuous and disciplined manner provides many advantages such as the accurate estimation of project costs and schedules as well as improving product and process qualities. This study aims to propose a model to predict the number of defects in the new version of a software product with respect to the previous stable version. The new version may contain changes related to a new feature or a modification in the algorithm or bug fixes. Our proposed model aims to predict the new defects introduced into the new version by analyzing the types of changes in an objective and formal manner as well as considering the lines of code (LOC) change. Defect predictors are helpful tools for both project managers and developers. Accurate predictors may help reducing test times and guide developers towards implementing higher quality codes. Our proposed model can aid software engineers in determining the stability of software before it goes on production. Furthermore, such a model may provide useful insight for understanding the effects of a feature, bug fix or change in the process of defect detection.

Assessment and cross-product prediction of software product line quality: accounting for reuse across products, over multiple releases

“…Numerical, post-release fault prediction studies of other software products not related to Eclipse include (Bibi et al 2006;Kastro and Bener 2008;Khoshgoftaar and Munson 1990;Li et al 2006;Nagappan et al 2006;Ostrand et al 2004Ostrand et al , 2005Ostrand et al , 2010Bell et al 2006;Weyuker et al 2008;Shin et al 2009). Bibi et al (2006) compared twelve different models to determine the benefits of regression via classification.…”

Using Negative Binomial Regression Analysis to Predict Software Faults: A Study of Apache Ant

“…Using negative binomial regression, we can estimate the value of dispersion parameter r and the parameters a, b 1 , b 2 , …, b n of variance λ by maximum likelihood method. Accordingly, Equation 1 can be used to predict the possibilities that certain number of bugs might exist in a module.…”

“…Accordingly, more testing efforts can be spent on software modules with positive predictions, which indicate the high possibilities of having bugs; and less effort can be allocated to modules with negative predictions, which indicate the low possibilities of having bugs. Considerable research has been performed in this area in recent years [1][2][3][4][5][6][7][8][9][10][11] .…”

Defect Prediction Leads to High Quality Product

“…• Traditionally, some defect prediction models are used to identify the number of defects in a multiversion system but they are not platform and language independent [15].…”

“…In [15] Metric data include CVS level data, change level data and previous version data was collected. Collected data was organized, consolidate and normalized.…”