Testing effort dependent software reliability model for imperfect debugging process considering both detection and correction

This paper studies the fault detection process (FDP) and fault correction process (FCP) with the incorporation of testing effort function and imperfect debugging. In order to ensure high reliability, it is essential for software to undergo a testing phase, during which faults can be detected and corrected by debuggers. The testing resource allocation during this phase, which is usually depicted by the testing effort function, considerably influences not only the fault detection rate but also the time to correct a detected fault. In addition, testing is usually far from perfect such that new faults may be introduced. In this paper, we first show how to incorporate testing effort function and fault introduction into FDP and then develop FCP as delayed FDP with a correction effort. Various specific paired FDP and FCP models are obtained based on different assumptions of fault introduction and correction effort. An illustrative example is presented. The optimal release policy under different criteria is also discussed

Modeling and analysis of software fault detection and correction process by considering time dependency

IEEE Transactions on Reliability564629-642IEER

Software Reliability Growth Models from the Perspective of Learning Effects and Change-Point.

Increased attention towards reliability of software systems has led to the thorough analysis of the process of reliability growth for prediction and assessment of software reliability in the testing or debugging phase. With many frameworks available in terms of the underlying probability distributions like Poisson process, Non-Homogeneous Poisson Process (NHPP), Weibull, etc, many researchers have developed models using the Non-Homogeneous Poisson Process (NHPP) analytical framework. The behavior of interest, usually, is S-shaped or exponential shaped. S-shaped behavior could relate more closely to the human learning. The need to develop different models stems from the fact that nature of the underlying environment, learning effect acquisition during testing, resource allocations, application and the failure data itself vary. There is no universal model that fits everywhere to be called an Oracle. Learning effects that stem from the experiences of the testing or debugging staff have been considered for the growth of reliability. Learning varies over time and this asserts need for conduct of more research for study of learning effects.Digital copy of ThesisUniversity of Kashmi

Fault detection and correction modeling of software systems

Ph.DDOCTOR OF PHILOSOPH

Software reliability modeling and release time determination

Ph.DDOCTOR OF PHILOSOPH

Structuring NPD processes: advancements in test scheduling and activity sequencing

Ph.DDOCTOR OF PHILOSOPH