Available soon...

Available soon...

Testing a large software program is a time consuming operation. In addition to the time spent by the tester in identifying, locating, and correcting bugs, a significant amount of time is spent in the execution of the program under test and its instrumented or fault-induced variants, also known as mutants. When using mutation testing to achieve high reliability, there can be many such mutants. In this article, we show how a multiple instruction multiple data (MIMD) architecture can be exploited to obtain significant reductions in the total execution time of the mutants. We describe the architecture of the PM othra system, which is designed to provide the tester with a transparent interface to a parallel machine. Experimental results obtained on the Ncube/7 hypercube are presented. The near-linear speedups show the perfect match that exists between the software testing application and a local memory MIMD architecture typified by the Ncube/7 machine. The compilation bottleneck, which could have an adverse effect on the speedup, is illustrated by experimental results.

Available soon...

Available soon...

Mutation analysis is a powerful technique for assessing and improving the quality of test data used to unit test software. Unfortunately, current automated mutation analysis systems suffer from severe performance problems. This paper presents a new method for performing mutation analysis that uses program schemata to encode all mutants for a program into one metaprogram, which is subsequently compiled and run at speeds substantially higher than achieved by previous interpretive systems. Preliminary performance improvements of over 300% are reported. This method has the additional advantages of being easier to implement than interpretive systems, being simpler to port across a wide range of hardware and software platforms, and using the same compiler and run-time support system that is used during development and/or deployment.

This paper compares the fault-detecting ability of several software test data adequacy criteria. It has previously been shown that if C/sub 1/ properly covers C/sub 2/, then C/sub 1/ is guaranteed to be better at detecting faults than C/sub 2/, in the following sense: a test suite selected by independent random selection of one test case from each subdomain induced by C/sub 1/ is at least as likely to detect a fault as a test suite similarly selected using C/sub 2/. In contrast, if C/sub 1/ subsumes but does not properly cover C/sub 2/, this is not necessarily the case. These results are used to compare a number of criteria, including several that have been proposed as stronger alternatives to branch testing. We compare the relative fault-detecting ability of data flow testing, mutation testing, and the condition-coverage techniques, to branch testing, showing that most of the criteria examined are guaranteed to be better than branch testing according to two probabilistic measures. We also show that there are criteria that can sometimes be poorer at detecting faults than substantially less expensive criteria.

Available soon...

Several relationships between software testing criteria, each induced by a relation between the corresponding multisets of subdomains, are examined. The authors discuss whether for each relation R and each pair of criteria, C/sub 1/ and C/sub 2/, R(C/sub 1/, C/sub 2/) guarantees that C/sub 1/ is better at detecting faults than C/sub 2/ according to various probabilistic measures of fault-detecting ability. It is shown that the fact that C/sub 1/ subsumes C/sub 2/ does not guarantee that C/sub 1/ is better at detecting faults. Relations that strengthen the subsumption relation and that have more bearing on fault-detecting ability are introduced.

Available soon...