Software reliability prediction and management: A multiple change‐point model approach

Abstract

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.

Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures

“…The constant B of Equation ( 11) is on the left side of Equation ( 17), whereas the constant A of Equation ( 11) is on the right side of Equation (17). In addition, N(t) in Equation ( 17) is the probability ratio p 1 /p 0 .…”

“…Raheem et al [15] devised an optimal release policy based on an SRGM considering imperfect debugging. Minamino et al [16] and Ke et al [17] introduced an optimal release policy based on a change-point model. Several studies have been conducted on software reliability using various approaches.…”

Utilizing Multi-Objective Evolutionary Algorithms to Optimize Open Source Software Release Management

“…The various phases in the development cycle may lead to variations in the testing environment, testing strategy, and resource requirements. A change-point refers to a significant shift in the software testing time [10]. To capture the change in software error occurrence or error-detection patterns, distinct values represent the detection rate at different phases.…”

Reliability model of open source software considering fault introduction with generalized inflection S-shaped distribution

“…Wang and Zhang [16] used a deep learning method with RNN encoder-decoder to predict software reliability. Ke and Huang [17] developed software reliability models based on changing points of testing efforts.…”