Approximating Deployment Metrics to Predict Field Defects and Plan Corrective Maintenance Activities

Snipes, Will; Robinson, B. L.; Brooks, Penelope A.

doi:10.1109/issre.2009.16

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…They only discussed the effect of the cycle time on average defect discovery rate, but did not discuss the shape of the defect curve. Most closely related to our work, is the work by Snipes et al [11] on predicting the parameters of the Yamada reliability model using pre-deployment metrics which also included cycle time.…”

Section: Related Workmentioning

confidence: 97%

Characterizing defect trends in software support

Duesterwald¹,

Klinger²,

Santhanam³

et al. 2014

Companion Proceedings of the 36th International Conference on Software Engineering

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We present an empirical analysis of defect arrival data in the operational phase of multiple software products. We find that the shape of the defect curves is sufficiently determined by three external and readily available release cycle attributes: the product type, the license model, and the cycle time between releases. This finding provides new insights into the driving forces affecting the specifics of defect curves and opens up new opportunities for software support organizations to reduce the cost of maintaining defect arrival models for individual products. In addition, it allows the possibility of predicting the defect arrival rate of one product from another with similar known attributes.

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Section: Related Workmentioning

confidence: 97%

Characterizing defect trends in software support

Duesterwald¹,

Klinger²,

Santhanam³

et al. 2014

Companion Proceedings of the 36th International Conference on Software Engineering

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“…The primitive components did not get annotated with failure probabilities and are therefore assumed to be perfectly reliable, which is reasonable for the scope of our study. Model Validation To get a first hint of the plausibility of the subsystem failure probabilities predicted by the LV model, we searched for a correlation between code metrics and failure probabilities [43,54]. We compared the subsystem failure probabilities and the arithmetic average cyclomatic complexity per method [38], which had been used in former studies.…”

Section: )mentioning

confidence: 99%

Performance and reliability prediction for evolving service-oriented software systems

et al. 2012

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During software system evolution, software architects intuitively trade off the different architecture alternatives for their extra-functional properties, such as performance, maintainability, reliability, security, and usability. Researchers have proposed numerous model-driven prediction methods based on queuing networks or Petri nets, which claim to be more cost-effective and less error-prone than current practice. Practitioners are reluctant to apply these methods because of the unknown prediction accuracy and work effort. We have applied a novel model-driven prediction method called Q-ImPrESS on a large-scale process control system from ABB consisting of several million lines of code. This paper reports on the achieved performance prediction accuracy and reliability prediction sensitivity analyses as well as the effort in person hours for achieving these results.

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“…To get a first hint of the plausibility of the subsystem failure probabilities predicted by the LV model, we searched for a correlation between code metrics and failure probabilities [21,27]. We compared the subsystem failure probabilities and the arithmetic average cyclomatic complexity per method [19], which had been used in former studies.…”

Section: Reliability Analysismentioning

confidence: 99%

An industrial case study on quality impact prediction for evolving service-oriented software

Koziolek¹,

Schlich²,

Bilich³

et al. 2011

Proceedings of the 33rd International Conference on Software Engineering

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Systematic decision support for architectural design decisions is a major concern for software architects of evolving service-oriented systems. In practice, architects often analyse the expected performance and reliability of design alternatives based on prototypes or former experience. Modeldriven prediction methods claim to uncover the tradeoffs between different alternatives quantitatively while being more cost-effective and less error-prone. However, they often suffer from weak tool support and focus on single quality attributes. Furthermore, there is limited evidence on their effectiveness based on documented industrial case studies. Thus, we have applied a novel, model-driven prediction method called Q-ImPrESS on a large-scale process control system consisting of several million lines of code from the automation domain to evaluate its evolution scenarios. This paper reports our experiences with the method and lessons learned. Benefits of Q-ImPrESS are the good architectural decision support and comprehensive tool framework, while one drawback is the time-consuming data collection.

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Approximating Deployment Metrics to Predict Field Defects and Plan Corrective Maintenance Activities

Cited by 5 publications

References 12 publications

Characterizing defect trends in software support

Characterizing defect trends in software support

Performance and reliability prediction for evolving service-oriented software systems

An industrial case study on quality impact prediction for evolving service-oriented software

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