Investigating the accuracy of defect estimation models for individuals and teams based on inspection data

Biffl, Stefan; Halling, Michael; Köszegi, Sabine

doi:10.1109/isese.2003.1237982

Cited by 7 publications

(7 citation statements)

References 19 publications

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“…It is noteworthy that all studies with significant results include one or more of the authors of the original paper [17]. Furthermore, both studies significantly in favour of checklist-based reading over perspective-based reading include one joint author [22] and [25]. These observations are aligned with those by Ciolkowski [10].…”

Section: Illustration Of Modelsupporting

confidence: 76%

An Evidence Profile for Software Engineering Research and Practice

Wohlin

2013

Perspectives on the Future of Software Engineering

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Evidence-based software engineering has emerged as an important part of software engineering. The need for empirical evaluation and hence evidence has grown in the last couple of decades when developing new models, methods, techniques and tools in research. Furthermore, industrial decision-making ought to become more evidence-based. The objective here is to develop and present an evidence-based profile, which could be used to divide pieces of evidence into different types and hence creating an overall picture of evidence in a specific case. The evidence profile is developed in such a way that it allows evidence to be judged in context. The evidence profile consists of five types of evidence, and the profile is illustrated for perspective-based reading. It is shown how pieces of evidence can be classified into the different types. It is concluded that this type of approach may be useful to capture the evidence with respect to a specific topic and in a specific context. The further work includes applying the evidence profile for evidence collected from different type of studies and contexts.

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Section: Illustration Of Modelsupporting

confidence: 76%

An Evidence Profile for Software Engineering Research and Practice

Wohlin

2013

Perspectives on the Future of Software Engineering

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“…classification (Menzies et al 2007a;Tosun et al 2008), ranking (Ohlsson and Wohlin 1998) and data used (i.e. source code (Menzies et al 2007a), documents (Biffl et al 2003). A previous study conducted by Runeson et al (2001) showed that it is difficult for companies to build a defect predictor that provides the best performance.…”

Section: Introductionmentioning

confidence: 98%

An industrial case study of classifier ensembles for locating software defects

2011

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As the application layer in embedded systems dominates over the hardware, ensuring software quality becomes a real challenge. Software testing is the most timeconsuming and costly project phase, specifically in the embedded software domain. Misclassifying a safe code as defective increases the cost of projects, and hence leads to low margins. In this research, we present a defect prediction model based on an ensemble of classifiers. We have collaborated with an industrial partner from the embedded systems domain. We use our generic defect prediction models with data coming from embedded projects. The embedded systems domain is similar to mission critical software so that the goal is to catch as many defects as possible. Therefore, the expectation from a predictor is to get very high probability of detection (pd). On the other hand, most embedded systems in practice are commercial products, and companies would like to lower their costs to remain competitive in their market by keeping their false alarm (pf) rates as low as possible and improving their precision rates. In our experiments, we used data collected from our industry partners as well as publicly available data. Our results reveal that ensemble of classifiers significantly decreases pf down to 15% while increasing precision by 43% and hence, keeping balance rates at 74%. The cost-benefit analysis of the proposed model shows that it is enough to inspect 23% of the code on local datasets to detect around 70% of defects.

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“…The moderator has the freedom to choose whether s/he is going to ask for a new meeting). Other stopping criteria can also be considered [1][5] [6].…”

Section: Reworkmentioning

confidence: 99%

Software Defects: Stay Away from Them. Do Inspections!

Travassos

2014

2014 9th International Conference on the Quality of Information and Communications Technology

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All software system fails. However, a development team can identify and remove as probability of software to fail. Identifying and throughout the software development proce important approach to improve software qu evidence about the feasibility, efficiency and software inspections to support the identific Besides their low cost, the performance of in positively influenced by the technique used Despite all these benefits, the use of inspectio place in the software industry. Therefore, this Q speech intends to discuss the benefits of Softwa method and techniques) and their use in software

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Investigating the accuracy of defect estimation models for individuals and teams based on inspection data

Cited by 7 publications

References 19 publications

An Evidence Profile for Software Engineering Research and Practice

An Evidence Profile for Software Engineering Research and Practice

An industrial case study of classifier ensembles for locating software defects

Software Defects: Stay Away from Them. Do Inspections!

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