Journal Article10.1007/S10489-009-0193-8
Mining software defect data to support software testing management
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TL;DR: An empirical approach, based on the analysis of defect data, that provides support for software testing management in two ways: construction of a predictive model for defect repair times, and a method for assessing testing quality across multiple releases is proposed.
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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 well-established 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%.
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Citations
Survey on software defect prediction techniques
TL;DR: This work is planning to develop an efficient approach for software defect prediction by using soft computing based machine learning techniques which helps to predict optimize the features and efficiently learn the features.
A feature dependent Naive Bayes approach and its application to the software defect prediction problem
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TL;DR: By following preprocessing steps, a Feature Dependent Naive Bayes (FDNB) classification method is proposed and it is demonstrated that to be reliable, a learning model must be constructed by using only training data, as otherwise misleading results arise from the use of the entire data set.
147
A survey on software fault detection based on different prediction approaches
Golnoush Abaei,Ali Selamat +1 more
TL;DR: According to this study, random forest provides the best prediction performance for large data sets and Naïve Bayes is a trustable algorithm for small data sets even when one of the feature selection techniques is applied.
88
Mining top-k frequent patterns with combination reducing techniques
Gwangbum Pyun,Unil Yun +1 more
TL;DR: A new concept, the composite pattern, is defined, and novel techniques for reducing pattern combinations in the single-path are proposed, where the former is CRM (Combination Reducing method), applying the authors' reduction manner, and the latter is CRMN ( Combination Red reducing method for N-itemset), considering N- itemset.
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Test strategies in distributed software development environments
TL;DR: The test strategies adopted in the software development lifecycle by a service provider pursuing distributed software development in New Zealand, Australia, and India are discussed, revealing that strategies are based on protection of sensitive data through management of test database, use of drivers and interfacing stub supports between modules, as well as compliance verification on incremental releases through a customized ''Synchronize and Stabilize'' lifecycle model.
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