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Software reliability prediction and management: A multiple change‐point model approach
Author(s) -
Ke SyuanZao,
Huang ChinYu
Publication year - 2020
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2653
Subject(s) - reliability engineering , computer science , software quality , software development , systems development life cycle , software reliability testing , software sizing , software development process , software metric , software , verification and validation , reliability (semiconductor) , software construction , engineering , operations management , programming language , power (physics) , physics , quantum mechanics
It is commonly recognized that software development is highly unpredictable and software quality may not be easily enhanced after software product is finished. During the software development life cycle (SDLC), project managers have to solve many technical and management issues, such as high failure rate, cost over‐run, low quality, and late delivery. Consequently, in order to produce robust and reliable software product(s) on time and within budget, project managers and developers have to appropriately allocate limited time, manpower, development, and testing effort. In the past, the distribution of testing effort or manpower can typically be described by the Weibull or Rayleigh model. Practically, it should be noticed that development environments or methods could be changed due to some reasons. Thus, when we plan to perform software reliability modeling and prediction, these changes or variations occurring in the development process have to be taken into consideration. In this paper, we will study how to use the Parr‐curve model with multiple change‐points to depict the consumption of testing effort and how to perform further software reliability analysis. Some mathematical properties of proposed model will be given and discussed. The applicability and performance of our proposed model will be demonstrated and assessed through real software failure data. Experimental results are analyzed and compared with other existing models to show that our proposed model gives better predictions. Finally, an optimal software release policy based on cost‐reliability criteria is proposed and studied. The main purpose is aimed at minimizing the total cost of software development when a reliability objective is given.