Enhancing the accuracy of software reliability prediction through quantifying the effect of test phase transitions

Li, Yan-Fu

doi:10.1016/j.amc.2012.08.083

Cited by 5 publications

(6 citation statements)

References 26 publications

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“…Irrespective of the estimation technique adopted, some degree of uncertainty will be associated with the predictions . Thus, the confidence intervals are often used to describe the range of values into which an estimation is expected to fall.…”

Section: Estimation and Analysismentioning

confidence: 99%

“…Thus, the confidence intervals are often used to describe the range of values into which an estimation is expected to fall. For a Poisson‐type model of the finite failures category, the 100 × (1 − α ) % confidence intervals for the mean value function can be approximated as follows:

U p p e r b o u n d = m (t) + k_{1 - α / 2} \sqrt{m (t)} and L o w e r b o u n d = m (t) - k_{1 - α / 2} \sqrt{m (t)},

where k 1 − α /2 is the 100 × (1 − α /2)‐th percentile of a standard normal distribution. As a consequence, the 80% upper bound and lower bound for our proposed model with L layers of faults can be calculated in the following way:

m false(t false) \pm k_{0.90} \sqrt{m false(t false)} = true \sum_{l = 1}^{L} a_{l} ((), 1 - \underset{e^{false{\cdot false}} f o r l t i m e s}{\underset{⏟}{e^{\cdot^{\cdot^{\cdot^{e^{\frac{r_{3}}{r_{2}} false(e^{\frac{r_{2}}{r_{1}} false(e^{- r_{1} t} - 1 false)} - 1 false)} - 1}}}}}}) \pm k_{0.90} \sqrt{true \sum_{l = 1}^{L} a_{l} ((), 1 - e^{\cdot^{\cdot^{\cdot^{e^{\frac{r_{3}}{r_{2}} false(e^{\frac{r_{2}}{r_{1}} false(e^{- r_{1} t} - 1 false)} - 1 false)} -}}}})}

…”

Section: Estimation and Analysismentioning

confidence: 99%

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Impacts of networking effects on software reliability growth processes: A multi‐attribute utility theory approach

Wen

2019

Qual Reliab Engng Int

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Software reliability growth models, which are based on nonhomogeneous Poisson processes, are widely adopted tools when describing the stochastic failure behavior and measuring the reliability growth in software systems. Faults in the systems, which eventually cause the failures, are usually connected with each other in complicated ways. Considering a group of networked faults, we raise a new model to examine the reliability of software systems and assess the model's performance from real-world data sets. Our numerical studies show that the new model, capturing networking effects among faults, well fits the failure data. We also formally study the optimal software release policy using the multi-attribute utility theory (MAUT), considering both the reliability attribute and the cost attribute. We find that, if the networking effects among different layers of faults were ignored by the software testing team, the best time to release the software package to the market would be much later while the utility reaches its maximum. Sensitivity analysis is further delivered. KEYWORDS multi-attribute utility theory, networking effects, nonhomogeneous poisson processes, software release policy, software reliability growth model 1952

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“…Over past three decades, many Software Reliability Growth Models (SRGMs) have been proposed to evaluate software reliability based on real collected failure data [5][6][7][8][9][10] and they can provide quantitative information about how to enhance the desired reliability of software products and improve the process of software development and testing. During SDLC, project managers have to allocate development and/or testing effort systematically to maximize the software reliability and to minimize potential failure penalties [11].…”

Section: Introductionmentioning

confidence: 99%

Software reliability analysis considering the variation of testing-effort and change-point

Ke¹,

Huang

Peng

2014

Proceedings of the International Workshop on Innovative Software Development Methodologies and Practices

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It is commonly recognized that software development is highly unpredictable and software quality may not be easily enhanced after software product is finished. During the software development life cycle (SDLC), project managers have to solve many technical and management issues, such as high failure rate, cost over-run, low quality, late delivery, etc. Consequently, in order to produce robust and reliable software product(s) on time and within budget, project managers and developers have to appropriately allocate limited development-and testing-effort and time. In the past, the distribution of testing-effort or manpower can typically be described by the Weibull or Rayleigh model. Practically, it should be noticed that development environments or methods could change due to some reasons. Thus when we plan to perform software reliability modeling and prediction, these changes or variations occurring in the development process have to be taken into consideration. In this paper, we will study how to use the Parr-curve model with multiple change-points to depict the consumption of testing-effort and how to perform further software reliability analysis. The applicability and performance of our proposed model will be demonstrated and assessed through real software failure data. Experimental results are analyzed and compared with other existing models to show that our proposed model gives better predictions.

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“…Lin [18] considered the influence of test phase transitions to quantify the variations in the effect of different test phases and discussed a software reliability modeling framework.…”

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confidence: 99%

Software reliability growth modeling with dynamic faults and release time optimization using GA and MAUT

Pachauri

Kumar

Dhar

2014

Applied Mathematics and Computation

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Enhancing the accuracy of software reliability prediction through quantifying the effect of test phase transitions

Cited by 5 publications

References 26 publications

Impacts of networking effects on software reliability growth processes: A multi‐attribute utility theory approach

Software reliability analysis considering the variation of testing-effort and change-point

Software reliability growth modeling with dynamic faults and release time optimization using GA and MAUT

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