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Software Defects (software + defect)

Distribution by Scientific Domains

Information Science and Computing	50%

Selected Abstracts

A fuzzy-based multimodel system for reasoning about the number of software defects

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 11 2005
Marek Reformat
Software maintenance engineers need tools to support their work. To make such tools relevant, they should provide engineers with quantitative input, as well as the knowledge needed to understand factors influencing maintenance activities. This article proposes an approach leading to multitechnique knowledge extraction and development of a comprehensive meta-model prediction system in the area of corrective maintenance. It dwells on elements of evidence theory and a number of fuzzy-based models. The models are developed using an evolutionary-based approach with different objectives applied to different subsets of data. Evidence theory,based Transferable Belief Model and belief function values assigned to generated models are used for reasoning purposes. The study comprises a detailed case for estimating the number of defects in a medical imaging system. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1093,1115, 2005. [source]

Modeling software evolution defects: a time series approach

JOURNAL OF SOFTWARE MAINTENANCE AND EVOLUTION: RESEARCH AND PRACTICE, Issue 1 2009
Uzma Raja
Abstract The Department of Information Systems, Statistics and Management Science, prediction of software defects and defect patterns is and will continue to be a critically important software evolution research topic. This study presents a time series analysis of multi-organizational multi-project defects reported during ongoing software evolution efforts. Using data from monthly defect reports for eight open source software projects over five years, this study builds and tests time series models for each sampled project. The resulting model accounts for the ripple effects of defect detection and correction by modeling the autocorrelation of code defect data. The autoregressive integrated moving average model (0,1,1) was found to hold for all sampled projects and thus provide a basis for both descriptive and predictive software defect analysis that is computationally efficient, comprehensible, and easy to apply. The model may be used to evaluate and compare the reliability of candidate software solutions, and to facilitate planning for software evolution budget and time allocation. Copyright © 2008 John Wiley & Sons, Ltd. [source]

NHPP models for categorized software defects

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 6 2005
Zhaohui Liu
Abstract We develop NHPP models to characterize categorized event data, with application to modelling the discovery process for categorized software defects. Conditioning on the total number of defects, multivariate models are proposed for modelling the defects by type. A latent vector autoregressive structure is used to characterize dependencies among the different types. We show how Bayesian inference can be achieved via MCMC procedures, with a posterior prediction-based L -measure used for model selection. The results are illustrated for defects of different types found during the System Test phase of a large operating system software development project. Copyright © 2005 John Wiley & Sons, Ltd. [source]