Data collection and analysis for the reliability prediction and estimation of a safety critical system using AIRS

Chitra, S.; Thiagarajan, K.; Rajaram, M.

doi:10.1109/icccnet.2008.4787675

Cited by 2 publications

(2 citation statements)

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“…Another approach [6] defined a classifier (with 37 software metrics) and use it to classify the software modules as fault-none or fault-prone. They compared their works with others and concluded that their model has the best performance.…”

Section: Related Workmentioning

confidence: 99%

“…In relation to existing approaches, ours investigates how we can use CK metrics to predict reliability and relates to approach [6], with the difference that we use CK metrics instead of cyclomatic complexity, decision count, decision density, etc. and we predict a reliability value for each class in the project, instead of classifying the modules in two categories.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Reliability of Object-Oriented Systems Using a Neural Network

Budur

Șerban

Veșcan

2019

Studia UBB Informatica

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One of the most important quality attributes of computer systems is reliability, which addresses the ability of the software to perform its required function under stated conditions for a stated period of time. The paper aim is twofold. Firstly, the proposed approach explores how to define a metric to qualify the sub-aspects comprised in ISO 25010 regarding reliability as maturity and availability. Secondly, we investigate to what extent the internal structure of the system quantified by the Chidamber and Kemerer (CK) metrics may be used to predict reliability. The approach for prediction is a feed-forward neural network with back-propagation learning. The results indicate that CK metrics are promising in predicting reliability using a neural network method.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%