Modelling and Analysing of Software Defect Prevention Using ODC

Trivedi, Piyush; Pachori, Som

doi:10.14569/ijacsa.2010.010311

Cited by 6 publications

(7 citation statements)

References 5 publications

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“…Trivedi and Pachori [6] focus ODC methodology to implement defect prevention in software industry. Real world implementation of ODC provides better way to classify and detect defects.…”

confidence: 99%

“…Similarly, casual analysis is used for Root Cause Analysis (RCA) [5,3] which is the prime technique for identification of risks associated with distinctive types of defects. Orthogonal Defects Classification (ODC), devised by IBM, is a commonly used technique for unearthing and classifying defects in the software products [6,7,8]. Bean [9] recommends PACRAT (Performance and Continuous Re-Commissioning Analysis Tool) for creation, revision and archiving during defect prevention process.…”

Section: Introductionmentioning

confidence: 99%

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Contemporary Trends in Defect Prevention: A Survey Report

Faizan¹,

Khan²,

Ulhaq³

2012

IJMECS

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Most of the software projects fail to meet the desired level of quality and standards due to different types of defects introduced during the course of requirement solicitation, designing and development. These defects inexorably hinder the secure deployment or smooth operations of the software systems. One of the key reasons for this misfortune is the lack of proper defect prevention planning while formulating the software architecture. Defect prevention needs to be a thorough and critical phase because it has a direct impact on quality of the product which cannot be compromised. This paper looks into different defect prevention techniques and analyses them critically. The scope of this study is restricted to the identification of the modern trends in defect prevention.

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“…Trivedi and Pachori [6] focus ODC methodology to implement defect prevention in software industry. Real world implementation of ODC provides better way to classify and detect defects.…”

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Contemporary Trends in Defect Prevention: A Survey Report

Faizan¹,

Khan²,

Ulhaq³

2012

IJMECS

View full text Add to dashboard Cite

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“…It involves categorization of software modules into defective or non-defective that is denoted by a set of software complexity metrics by utilizing a classification model that is derived from earlier development projects data [9]. The metrics for software complexity may consist of code size [10], McCabe's cyclomatic complexity [11] and Halstead's Complexity [12].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Machine Learning Techniques for Software Defect Detection

Aleem¹,

Capretz²,

Ahmed³

2015

IJSEA

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Machine Learning approaches are good in solving problems that have less information. In most cases, the software domain problems characterize as a process of learning that depend on the various circumstances and changes accordingly. A predictive model is constructed by using machine learning approaches and classified them into defective and non-defective modules. Machine learning techniques help developers to retrieve useful information after the classification and enable them to analyse data from different perspectives. Machine learning techniques are proven to be useful in terms of software bug prediction. This study used public available data sets of software modules and provides comparative performance analysis of different machine learning techniques for software bug prediction. Results showed most of the machine learning methods performed well on software bug datasets.

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“…It provides a meaningful classification of defects into classes that collectively point to the process that needs attention [12]. Various case studies have shown that ODC can improve the effectiveness and efficiency of development and testing which is important for quality improvements [13] [14][15] [16]. In this paper, an attempt is made to present a comprehensive view of defect prevention techniques and analyses them critically by stating the advantages and limitations of selected approaches and inferring 3T"s of Defect Prevention through this study.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Effective Defect Prevention - 3T Approach

Chandani¹,

Gupta²

2014

IJIEEB

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Defects are most detrimental entities which deter the smooth operation and deployment of the software system and can arise in any part of the life cycle, they are most feared, but still Defect Prevention is mostly discounted field of software quality. Unattended defects cause a lot of rework and waste of effort. Hence only finding the defects is not important, finding the root cause of the defect is also important which is quite difficult due to levels of abstraction in terms of people, process, complexity, environment and other factors. Through this study various techniques of Defect classification, prevention and root cause analysis are analysed. The intent of this paper is to demonstrate the structured process showing defect prevention flow and inferring three T"s (Tracking, Technique and Training) after analysis.

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Modelling and Analysing of Software Defect Prevention Using ODC

Cited by 6 publications

References 5 publications

Contemporary Trends in Defect Prevention: A Survey Report

Contemporary Trends in Defect Prevention: A Survey Report

Benchmarking Machine Learning Techniques for Software Defect Detection

A Survey on Effective Defect Prevention - 3T Approach

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