A literature survey of the quality economics of defect-detection techniques

Wagner, Stefan

doi:10.1145/1159733.1159763

Cited by 53 publications

(34 citation statements)

References 26 publications

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“…It gives the NPT as an partitioned expression depending on the state of the parent node Appropriateness of Comments. For the average effort of a change, we refer to [15] that gave a mean defect removal cost of 27.4 person-hours with minimum 3.9 and maximum 66.6. Although a change does not always have to be a defect removal, it is precise enough for the initial evaluation.…”

Section: Tnormalmentioning

confidence: 99%

A Bayesian network approach to assess and predict software quality using activity-based quality models

Wagner

2010

Information and Software Technology

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Context: Software quality is a complex concept. Therefore, assessing and predicting it is still challenging in practice as well as in research. Activitybased quality models break down this complex concept into concrete definitions, more precisely facts about the system, process, and environment as well as their impact on activities performed on and with the system. However, these models lack an operationalisation that would allow them to be used in assessment and prediction of quality. Bayesian networks have been shown to be a viable means for this task incorporating variables with uncertainty. Objective: The qualitative knowledge contained in activity-based quality models are an abundant basis for building Bayesian networks for quality assessment. This paper describes a four-step approach for deriving systematically a Bayesian network from a assessment goal and a quality model. Method: The four steps of the approach are explained in detail and with running examples. Furthermore, an initial evaluation is performed, in which data from NASA projects and an open source system is obtained. The approach is applied to this data and its applicability is analysed. Results: The approach is applicable to the data from the NASA projects and the open source system. However, the predictive results vary depending on the availability and quality of the data, especially the underlying general distributions. Conclusion: The approach is viable in a realistic context but needs further investigation in case studies in order to analyse its predictive validity.

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Section: Tnormalmentioning

confidence: 99%

A Bayesian network approach to assess and predict software quality using activity-based quality models

Wagner

2010

Information and Software Technology

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“…The distributions are derived from the survey [38,37]. We base the analysis on an example software with 1000 LOC and with 10-15 faults.…”

Section: Input Factors and Datamentioning

confidence: 99%

A model and sensitivity analysis of the quality economics of defect-detection techniques

Wagner

2006

Proceedings of the 2006 International Symposium on Software Testing and Analysis

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One of the main cost factors in software development is the detection and removal of defects. However, the relationships and influencing factors of the costs and revenues of defect-detection techniques are still not well understood. This paper proposes an analytical, stochastic model of the economics of defect detection and removal to improve this understanding. The model is able to incorporate dynamic as well as static techniques in contrast to most other models of that kind. We especially analyse the model with state-ofthe-art sensitivity analysis methods to (1) identify the most relevant factors for model simplification and (2) prioritise the factors to guide further research and measurements.

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“…The indicator incorporates productivity through its impact on direct development costs and product quality through its impact on indirect or downstream costs associated with rework [4].…”

Section: Introductionmentioning

confidence: 99%

“…Padberg and Muller [8] used a similar approach in their own analysis of the same practice. Wagner [4] recently proposed an economic efficiency model for quality that aggregates costs and benefits of quality activities into a return-oninvestment metric.…”

Section: Introductionmentioning

confidence: 99%