Unified framework for developing testing effort dependent software reliability growth models

  • Authors:

  • P. K. Kapur;Omar Shatnawi;Anu G. Aggarwal;Ravi Kumar

  • Affiliations:

  • Department of Operational Research, University of Delhi, Delhi, India;Department of Computer Science, Al al-Bayt University, Mafraq, Jordan;Department of Operational Research, University of Delhi, Delhi, India;Department of Operational Research, University of Delhi, Delhi, India

  • Venue:
  • WSEAS TRANSACTIONS on SYSTEMS
  • Year:
  • 2009

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Abstract

Several software reliability growth models (SRGMs) have been presented in the literature in the last three decades. These SRGMs take into account different testing environment depending on size and efficiency of testing team, type of components and faults, design of test cases, software architecture etc. The plethora of models makes the model selection an uphill task. Recently, some authors have tried to develop a unifying approach so as to capture different growth curves, thus easing the model selection process. The work in this area done so far relates the fault removal process to the testing/execution time and does not consider the consumption pattern of testing resources such as CPU time, manpower and number of executed test cases. More realistic modeling techniques can result if the reliability growth process is studied with respect to the amount of expended testing efforts. In this paper, we propose a unified framework for testing effort dependent software reliability growth models incorporating imperfect debugging and error generation. The proposed framework represents the realistic case of time delays between the different stages of fault removal process i.e Failure Observation/Fault Detection and Fault Removal/Correction processes. The Convolution of probability distribution functions have been used to characterize time differentiation between these two processes. Several existing and new effort dependent models have been derived by using different types of distribution functions. We have also provided data analysis based on the actual software failure data sets for some of the models discussed and proposed in the paper.