Estimating the number of residual defects [in software]

Malaiya, Yashwant K.; Denton, J.

doi:10.1109/hase.1998.731600

Cited by 12 publications

(16 citation statements)

References 12 publications

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“…The parameters of reliability models are selected attributes of the particular program being studied, typically represented by software metrics. These models use characteristics in software codes such as lines of code, nesting of loops, external references, input/outputs, cyclomatic complexity and so forth to estimate the number of defects in the software [8][9][10][11][12][13][14][15]17,18,20,[24][25][26]38,39]. In two earlier studies, software development faults were predicted using object-oriented design metrics and SQL metrics [32,33].…”

Section: Related Workmentioning

confidence: 99%

Estimating software readiness using predictive models

Quah¹

2009

Information Sciences

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Section: Related Workmentioning

confidence: 99%

Estimating software readiness using predictive models

Quah¹

2009

Information Sciences

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“…These models are mostly described in 2 classes: one class is in the later period of the software life cycle (testing phase), having gotten defect data, predicts how many defects still in the software with these data. Models in this class include: capture-recapture method based model [10], neural network based model [11], measure method based on scale and complexity of source code [12,13]. The other class is before developing the software, predicts how many defects will be in the software develop process by analyzing on defect data in formerly projects.…”

Section: Software Defect Predictionmentioning

confidence: 99%

Research on software defect prediction based on data mining

Chen

Xiang-heng

et al. 2010

2010 the 2nd International Conference on Computer and Automation Engineering (ICCAE)

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As the development of computer technology, software system becomes more and more complicated. Because of human's ability limit, there must be a lot of defects generated in the software development life cycle. This paper reviewed the state of art in the field of software defect management and prediction, and presented data mining technology briefly. Finally, proposed an ideal software defect management and prediction system, researched and analyzed several software defect prediction methods based on data mining techniques and specific models (Bayesian Network and PRM). With this system, we can efficiently draw up some prevention and solution scheme to guide the development of new software.

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“…If the model assumptions apply and we can estimate the number of faults N at the time of release (e.g. using estimation methods such as [3], [4], [7], [9]) the reliability growth can be bounded at any time in the future. Note that the theory does not tell us when (or even if) the faults will be found, but it does set a quantitative bound on the probability of program failure after testing and this bound always decreases with increasing tests (or operating time).…”

Section: Worst Case Bound Theorymentioning

confidence: 99%

Rescaling reliability bounds for a new operational profile

Bishop¹

2002

Proceedings of the 2002 ACM SIGSOFT International Symposium on Software Testing and Analysis

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One of the main problems with reliability testing and prediction is that the result is specific to a particular operational profile. This paper extends an earlier reliability theory for computing a worst case reliability bound. The extended theory derives a re-scaled reliability bound based on the change in execution rates of the code segments in the program. In some cases it is possible to derive a maximum failure rate bound that applies to any change in the profile. It also predicts that (in principle) a "fair" test profile can be derived where the reliability bounds are relatively insensitive to the operational profile. In addition the theory allows unit and module test coverage measures to be incorporated into an operational reliability bound prediction. The implications of the theory are discussed, and the theory is evaluated by applying it to two example programs with known faults.

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Estimating the number of residual defects [in software]

Cited by 12 publications

References 12 publications

Estimating software readiness using predictive models

Estimating software readiness using predictive models

Research on software defect prediction based on data mining

Rescaling reliability bounds for a new operational profile

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