Exploring defect data from development and customer usage on software modules over multiple releases

Biyani, S.; Santhanam, P.

doi:10.1109/issre.1998.730896

Cited by 23 publications

(21 citation statements)

References 11 publications

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“…Furthermore, one can argue that as more fixes are applied to a component, new faults are introduced by the fixes and therefore the probability of finding a subsequent fault increases. Evidence of this was presented in a recent study [9] whereby it was observed that components with more faults pre-release also tend to have more faults post-release, leading to the conclusion that the number of faults already found is positively related of the number of faults to be found in the future. This is termed positive contagion.…”

Section: Number Of Faultsmentioning

confidence: 90%

“…We would expect that code churn should have at least a monotonically increasing relationship with maintenance effort if it is to be used as a surrogate measure. 9 We test this through a data set collected from a 40 KSLOC systems application written in C that had a peak staff load of 5 persons. Our focus is on corrective maintenance only, since this has been the focus of previous maintenance effort prediction studies as well [55].…”

Section: Appendix A: Evaluating Code Churnmentioning

confidence: 99%

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Methodology for Validating Software Product Metrics

Emam

Schneidewind²

2002

Encyclopedia of Software Engineering

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A large number of software product metrics have been proposed in software engineering. We use the term metric here to be consistent with prevailing international standards. Product metrics quantitatively characterize some aspect of the structure of a software product, such as a requirements specification, a design, or source code. They are also commonly collectively known as complexity metrics . While many of these metrics are based on good ideas about what is important to measure in software to capture its complexity, it is still necessary to systematically validate them. Two types of validation are recognized internal and external. Internal validation is a theoretical exercise that ensures that the metric is a proper numerical characterization of the property it claims to measure. Demonstrating that a metric measures what it purports to measure is a form of theoretical validation. Typically, one defines the properties of the attribute that is to be measured, for example, the properties of module coupling. Then one demonstrates analytically that the product metric satisfies these properties. External validation involves empirically demonstrating that the product nature is associated which is important external metter or an improvement is shown above. Internal and external validation are also commonly referred to as theoretical and empirical validation respectively This article presents a comprehensive methodology for validating software product metrics. Our focus will be limited to empirical validation.

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Section: Number Of Faultsmentioning

confidence: 90%

Section: Appendix A: Evaluating Code Churnmentioning

confidence: 99%

Methodology for Validating Software Product Metrics

Emam

Schneidewind²

2002

Encyclopedia of Software Engineering

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“…In other studies on fault measures , Ohlsson and Alberg [15] noted that in commercial products, the average cost of fixing an operational fault was $7000. Biyani and Santhanam [16] found correlation between the number of faults found in development and the number of faults remaining in operation. Ostrand et al [17] developed a negative binomial regression model to predict the number of faults in each file for many consecutive releases of a software.…”

Section: Related Workmentioning

confidence: 97%

An Empirical Investigation of Predicting Fault Count, Fix Cost and Effort Using Software Metrics

Shatnawi¹,

Li²

2016

ijacsa

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Abstract-Software fault prediction is important in software engineering field. Fault prediction helps engineers manage their efforts by identifying the most complex parts of the software where errors concentrate. Researchers usually study the faultproneness in modules because most modules have zero faults, and a minority have the most faults in a system. In this study, we present methods and models for the prediction of fault-count, fault-fix cost, and fault-fix effort and compare the effectiveness of different prediction models. This research proposes using a set of procedural metrics to predict three fault measures: fault count, fix cost and fix effort. Five regression models are used to predict the three fault measures. The study reports on three data sets published by NASA. The models for each fault are evaluated using the Root Mean Square Error. A comparison amongst fault measures is conducted using the Relative Absolute Error. The models show promising results to provide a practical guide to help software engineers in allocating resources during software testing and maintenance. The cost fix models show equal or better performance than fault count and effort models.

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“…In fact, what defect density before release tells us is how extensive testing was. Using these observations, Biyani and Santhanam [1] illustrate how defect data can be used to estimate the quality of released software by examining the relationship between numbers of pre-release and post-release defects. Our method to evaluate testing quality is similar to the work of Biyani and Santhanam.…”

Section: Related Workmentioning

confidence: 99%

Mining software defect data to support software testing management

Hewett

2009

Appl Intell

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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%.

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Exploring defect data from development and customer usage on software modules over multiple releases

Cited by 23 publications

References 11 publications

Methodology for Validating Software Product Metrics

Methodology for Validating Software Product Metrics

An Empirical Investigation of Predicting Fault Count, Fix Cost and Effort Using Software Metrics

Mining software defect data to support software testing management

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