Defect Prediction Leads to High Quality Product

Azeem, Naheed; Usmani, Shazia

doi:10.4236/jsea.2011.411075

Cited by 12 publications

(3 citation statements)

References 20 publications

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“…Instead, reliability is ensured by an upfront defect prevention cost rather than an after-the-fact defect detection effort. This is also a strategy frequently employed by software product companies that are able to use model-based testing and other quality assurance strategies prior to end use (Becker, 2002;Azeem, 2011;Coverity, 2012) Rocket Motors. One rocket motor manufacturer's aging and T&E program includes a mechanistic and an empirical program, based on the differences between causality and knowledge.…”

Section: Identifying Tande Strategies Used By Industrymentioning

confidence: 99%

Applicability of Industrial Test and Evaluation Practices to Future Nuclear Weapons

Young

2015

INCOSE International Symp

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This report presents trade study methodology for alternative test and evaluation (T&E) strategies that provides some early indicators of efficacy by application of the method to future nuclear weapons (NWs). Identification of strategies for trade studies can be hampered by subjectivity or incompleteness, effectively limiting the scope of such studies by unconscious bias. This methodology seeks to formalize the identification process by which alternatives, evaluation criteria and weighting factors are derived. The method identifies characteristics specific to the example system, identifies products or engineered systems that share some of those characteristics and a description of the T&E strategies used by these industrial analogues. Application of industry paradigms to NW T&E results in some obvious challenges, given the distinct business environments and product requirements involved in the nuclear weapons enterprise. However, a framework for performing trade studies can be generalized for use by others using the methodology developed for this study.

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Section: Identifying Tande Strategies Used By Industrymentioning

confidence: 99%

Applicability of Industrial Test and Evaluation Practices to Future Nuclear Weapons

Young

2015

INCOSE International Symp

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“…The quality of software is directly related to the number of defects and changes in the software product. Hence change and defect prediction is very important in this field [7].…”

Section: Introductionmentioning

confidence: 99%

CMS tool

Malhotra

Agrawal

2014

SIGSOFT Softw. Eng. Notes

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Defect and change prediction is a very important activity in software development. Predicting erroneous classes of the system early in the software development life cycle will enable early identification of risky classes in the initial phases. This will assist software practitioners in designing and developing software systems of better quality with focused resources and hence take necessary corrective design actions. In this work we describe a framework to develop and calculate the defect fixes and changes made during various versions of a software system. We develop a tool, Configuration Management System (CMS), which uses log files obtained from a Concurrent Versioning System (CVS) repository in order to collect the number of defects from each class. The tool also calculates the number of changes made during each version of the software. This tool will also assist software practitioners and researchers in collecting defect and change data for software systems.

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“…Defects in software projects lead to failures that increase the total cost of the project. From software developer point of view, usage of efficient fault investigation methods is important to predict defect-prone modules and thus improve software quality [1][2][3]. Software prediction models make use of historical software project faults and its corresponding software metrics to identify quality of an upcoming project/module from similar domain.…”

Section: Introductionmentioning

confidence: 99%

Majority vote feature selection algorithm in software fault prediction

Borandağ¹,

Özçift²,

Kılınç³

et al. 2019

ComSIS

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Identification and location of defects in software projects is an important task to improve software quality and to reduce software test effort estimation cost. In software fault prediction domain, it is known that 20% of the modules will in general contain about 80% of the faults. In order to minimize cost and effort, it is considerably important to identify those most error prone modules precisely and correct them in time. Machine Learning (ML) algorithms are frequently used to locate error prone modules automatically. Furthermore, the performance of the algorithms is closely related to determine the most valuable software metrics. The aim of this research is to develop a Majority Vote based Feature Selection algorithm (MVFS) to identify the most valuable software metrics. The core idea of the method is to identify the most influential software metrics with the collaboration of various feature rankers. To test the efficiency of the proposed method, we used CM1, JM1, KC1, PC1, Eclipse Equinox, Eclipse JDT datasets and J48, NB, K-NN (IBk) ML algorithms. The experiments show that the proposed method is able to find out the most significant software metrics that enhances defect prediction performance.

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Defect Prediction Leads to High Quality Product

Abstract: ABSTRACT

Cited by 12 publications

References 20 publications

Applicability of Industrial Test and Evaluation Practices to Future Nuclear Weapons

Applicability of Industrial Test and Evaluation Practices to Future Nuclear Weapons

CMS tool

Majority vote feature selection algorithm in software fault prediction

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