Mining software defect data to support software testing management

Abstract

Achieving high quality software would be easier if effective software development practices were known and deployed in appropriate contexts. Because our theoretical knowledge of the underlying principles of software development is far from complete, empirical analysis of past experience in software projects is essential for acquiring useful software practices. As advances in software technology continue to facilitate automated tracking and data collection, more software data become available. Our research aims to develop methods to exploit such data for improving software development practices.This paper proposes an empirical approach, based on the analysis of defect data, that provides support for software testing management in two ways: (1) construction of a predictive model for defect repair times, and (2) a method for assessing testing quality across multiple releases. The approach employs data mining techniques including statistical methods and machine learning. To illustrate the proposed approach, we present a case study using the defect reports created during the development of three releases of a large medical software system, produced by a large wellestablished software company. We validate our proposed testing quality assessment using a statistical test at a significance level of 0.1. Despite the limitations of the available data, our predictive models give accuracies as high as 93%.

Defect Prediction Leads to High Quality Product

“…• Prior research on defect prediction fails to fully utilize the defect data and defect repair time estimation requires mathematical assumptions [19]. • Software prediction model only works well when enough amount of data is available in software repository within the organization to initially fed the model [20].…”

“…They used four data mining algorithms based on three different approaches i.e. decision tree learner, Naïve Bayes classifier and neural network approach used to build a mode [19]. Zimmermann findings are in the case of when there is not enough historical data to train the model.…”

“…-Different machine learning algorithm and data mining techniques are used to improve the defect prediction accuracy [16,18]. -Extracting automatically key information from the data repositories that are more relevant for defect prediction [7] and trying right set of metrics that influence the success of cross project predictions [19,20].…”

Extracting software static defect models using data mining

“…This includes the success factors of software projects that attracted researchers a long time ago [1], the support of software testing management [2] and the defect pattern discovery [3]. The software defects estimation and prediction processes are used in the analysis of software quality [4].…”

“…In [2,3,15], data mining techniques are used to search for rules that indicate modules with a high probability of being defective. Other techniques include the use of SVM and Service oriented architecture using expert COCOMO [16,17].…”