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A Discrete NHPP Model for Software Reliability Growth with Imperfect Fault Debugging and Fault Generation

Volume 2, Number 4, October 2006 - Paper 5  - pp. 351 - 368

P. K. Kapur1, OM Pal Singh1, Omar Shatnawi2 and Anu Gupta1

1Department of Operational Research, University of Delhi, Delhi, India
2Department of Computer Science, Prince Hussein bin Abdullah Information Technology College, Mafraq, Jordan

(Received on February 10, 2006)


This paper presents a discrete software reliability growth model (SRGM) and introduces the concept of two types of imperfect debugging during software fault removal phenomenon with Logistic Fault removal rate. Most of the discrete SRGMs discussed in the literature seldom differentiate between the failure observation and fault removal processes. In real software development environment, the number of failures observed need not be same as the number of error removed. If the number of failures observed is more than the number of faults removed then we have the case of imperfect debugging. Due to the complexity of the software system and the incomplete understanding of the software requirements, specifications and structure, the testing team may not be able to remove the fault perfectly on the detection of the failure and the original fault may remain or get replaced by another fault. While the first phenomenon is known as imperfect fault debugging, the second is called fault generation. In case of imperfect fault debugging the fault content of the software is not changed, but just because of incomplete understanding of the software, the detected fault is not removed completely. But in case of error generation the fault content increases as the testing progresses and removal results in introduction of new faults while removing old ones. n. The model has been validated, evaluated and compared with other existing discrete NHPP models by applying it on actual failure / fault removal data sets cited from real software development projects. The results show that the proposed model provides improved goodness of fit and predictive validity for software failure / fault removal data.


References: 18

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