Title page for ETD etd-10112004-141426

Type of Document Master's Thesis
Author Kysetti, Praveen Babu
Author's Email Address pkyset1@lsu.edu
URN etd-10112004-141426
Title Optimal Test Case Selection for Multi-Component Software System
Degree Master of Science in Industrial Engineering (M.S.I.E.)
Department Industrial & Manufacturing Systems Engineering
Advisory Committee
Advisor Name Title
Xiaoyue Jiang Committee Chair
Charles McAllister Committee Member
Thomas Ray Committee Member
  • dynamic programming
  • modular software
  • software reliability
  • test case
  • Bayesian analysis
Date of Defense 2004-09-03
Availability unrestricted
The omnipresence of software has forced upon the industry to produce efficient software in a short time. These requirements can be met by code reusability and software testing. Code reusability is achieved by developing software as components/modules rather than a single block. Software coding teams are becoming large to satiate the need of massive requirements. Large teams could work easily if software is developed in a modular fashion. It would be pointless to have software that would crash often. Testing makes the software more reliable. Modularity and reliability is the need of the day.

Testing is usually carried out using test cases that target a class of software faults or a specific module. Usage of different test cases has an idiosyncratic effect on the reliability of the software system. Proposed research develops a model to determine the optimal test case policy selection that considers a modular software system with specific test cases in a stipulated testing time.

The proposed model, models the failure behavior of each component using a conditional NHPP (Non-homogeneous Poisson process) and the interactions of the components by a CTMC (continuous time Markov chain). The initial number of bugs and the bug detection rate are known distributions. Dynamic programming is used as a tool in determining the optimal test case policy. The complete model is simulated using Matlab.

The Markov decision process is computationally intensive but the implementation of the algorithm is meticulously optimized to eliminate repeat calculations. This has saved roughly 25-40% in processing time for different variations of the problem.

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