A binomial software reliability model based on coverage of structural testing criteria

Abstract

A new approach to software reliability modeling is discussed where variables indirectly related with software reliability are used to provide additional information for the modeling process. Previous studies, empirical and theoretical evidences, and results from experiments indicate that there is a strong relationship between software reliability and coverage of program elements required to be exercised by structural testing criteria. This paper develops a binomial type coverage-based software reliability model through the definition of a coverage-based failure rate function. The Binomial software reliability Model Based on Coverage-BMBC-is proposed and discussed. In the BMBC test data between failures is used instead of time as independent variable; the model was assessed with test data from a real application, making use of the following structural testing criteria: all-nodes, all-edges, and potential-uses-a data-flow based family of testing criteria. The results from our experiments have shown that our modeling approach has some advantages over some traditional reliability models and points to a very promising research direction in software reliability.

Virtual Coverage: A New Approach to Coverage-Based Software Reliability Engineering

“…The data set, called DS2, contains the values of block, branch, c-use and p-use coverages. Next, we consider a recent data set reported by Crespo et al (2008). This data set (called DS3) was collected from a software system developed by European Space Agency.…”

“…In order to relate such SRGMs to the testing time, Park and Fujiwara (2006), Park et al (2007Park et al ( , 2008a and Park and Lee (2010) studied CGFs. Crespo et al (2008Crespo et al ( , 2009) recently proposed new SRGMs that simultaneously incorporate testing time and coverage.…”

Selection of a Predictive Coverage Growth Function

“…In this section we illustrate the application of the proposed selection method to a real data set recently reported by Crespo et al (2008). The data set consists of five coverage metrics, which are respectively all-nodes(NODES), all-arcs(ARCS), allpotential-uses(PU), all-potential-uses/du(PUDU) and all-potential-du-paths(PDU).…”

“…However, it was recognized that the testing time was insufficient to express the fault detection and removal process. Attempts to integrate coverage information into SRGMs have been made by Gokhale et al (1996), Malaiya et al (2002), Pham and Zhang (2003), Park and Fujiwara (2006), Crespo et al (2008Crespo et al ( , 2009 and Park et al (2008b). Each coverage-based SRGM involves a coverage growth function(CGF) that describes the coverage growth process during testing.…”

Estimation of Coverage Growth Functions

“…Such a relationship is obtained by modeling the fault detection and removal process during testing; however, it was recognized that the testing time was not enough to express the fault detection and removal process. Attempts to integrate coverage information into SRGMs have been made by Gokhale et al (1996), Malaiya et al (2002), Pham and Zhang (2003), Park and Fujiwara (2006), Crespo et al (2008, 2009), and Park et al (2008b. Each coverage-based SRGM involves a coverage growth function(CGF), that describes the coverage growth process during testing.…”