A practical method for software quality control via program mutation

Wu, Di; Hennell, M.A.; Hedley, D.; Riddell, I. J.

doi:10.1109/wst.1988.5371

Cited by 11 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is similar to the test data adequacy of Wu et al, 38 but must be applied to specifications, not programs. Given a method, M, for creating a set of mutants, the score, S, is the number of mutants killed by the test set, k, divided by the total number of mutants, N , produced by M on r.…”

Section: A Specification Coverage Metricmentioning

confidence: 97%

“…If a mutant is found to be inconsistent by any of the test cases in the test set, it is considered to be killed by the test set. The ratio of killed mutants to total mutants is a coverage metric, similar to that of Wu et al, 38 but applied to specifications. The higher the ratio, the better or more completely the test set covers the specification.…”

Section: Computing a Coverage Metric With A Model Checkermentioning

confidence: 99%

See 1 more Smart Citation

A Specification-Based Coverage Metric to Evaluate Test Sets

Ammann

Black

2001

Int. J. Rel. Qual. Saf. Eng.

View full text Add to dashboard Cite

Software developers use a variety of formal and informal methods, including testing, to argue that their systems are suitable for building high assurance applications. In this paper, we develop another connection between formal methods and testing by defining a specification-based coverage metric to evaluate test sets. Formal methods in the form of a model checker supply the necessary automation to make the metric practical. The metric gives the software developer assurance that a given test set is sufficiently sensitive to the structure of an application's specification. We also develop the necessary foundation for the metric and then illustrate the metric on an example.

show abstract

Section: A Specification Coverage Metricmentioning

confidence: 97%

Section: Computing a Coverage Metric With A Model Checkermentioning

confidence: 99%

A Specification-Based Coverage Metric to Evaluate Test Sets

Ammann

Black

2001

Int. J. Rel. Qual. Saf. Eng.

View full text Add to dashboard Cite

show abstract

“…Software fault models are injected in the code by resorting to the concept of software mutants [30]. For each injected fault, the LLVM IR code is modified to generate a mutated version able to emulate the injection of the sfm during the execution.…”

Section: Software Architecture Layermentioning

confidence: 99%

SyRA: Early System Reliability Analysis for Cross-Layer Soft Errors Resilience in Memory Arrays of Microprocessor Systems

Vallero

Savino

Chatzidimitriou

et al. 2019

IEEE Trans. Comput.

View full text Add to dashboard Cite

Cross-layer reliability is becoming the preferred solution when reliability is a concern in the design of a microprocessor-based system. Nevertheless, deciding how to distribute the error management across the different layers of the system is a very complex task that requires the support of dedicated frameworks for cross-layer reliability analysis. This paper proposes SyRA, a system-level cross-layer early reliability analysis framework for radiation induced soft errors in memory arrays of microprocessor-based systems. The framework exploits a multi-level hybrid Bayesian model to describe the target system and takes advantage of Bayesian inference to estimate different reliability metrics. SyRA implements several mechanisms and features to deal with the complexity of realistic models and implements a complete toolchain that scales efficiently with the complexity of the system. The simulation time is significantly lower than micro-architecture level or RTL fault-injection experiments with an accuracy high enough to take effective design decisions. To demonstrate the capability of SyRA, we analyzed the reliability of a set of microprocessor-based systems characterized by different microprocessor architectures (i.e., Intel x86, ARM Cortex-A15, ARM Cortex-A9) running both the Linux operating system or bare metal in the presence of single bit upsets caused by radiation induced soft errors. Each system under analysis executes different software workloads both from benchmark suites and from real applications.

show abstract

“…The number of executions needed to achieve a confidence of approximately unity in block BB j will be related to the number of theoretical mutants of BB j . Given a comprehensively defmed set of mutant productions, (for example, see [5]) the theoretical number of possible mutants could be estimated statically and specific factors which govern the mutant death rate could also be assessed such as the degrees of polyjmultinomials [1,6]; code complexity; the number of variables active in the block; the number and values of boolean variables needed to kill all mutant boolean expressions [5]; the maximum dimension of arrays, etc.…”

Section: The Execution Of a Basic Block On One Path Many Timesmentioning

confidence: 99%

“…then the logical result of the predicate would differ on at least one of the test cases. Other test data requirements can be defined for other classes of errors [2,5].…”

mentioning

confidence: 99%

A conceptual model of software testing

Marshall

1991

Software Testing Verif & Rel

View full text Add to dashboard Cite

As code is executed correctly under test, confidence in the correctness of the code increases. In this context, an intuitive conceptual model of the process of software testing which draws upon experience gained with mutation analysis is presented. The model is used to explain how the testing of one path can influence confidence in other (possibly unexecuted) paths. It is also discussed in relation to software reliability and systematic structural testing, and is shown to be consistent with observations made during these forms of testing.

show abstract

A practical method for software quality control via program mutation

Cited by 11 publications

References 7 publications

A Specification-Based Coverage Metric to Evaluate Test Sets

A Specification-Based Coverage Metric to Evaluate Test Sets

SyRA: Early System Reliability Analysis for Cross-Layer Soft Errors Resilience in Memory Arrays of Microprocessor Systems

A conceptual model of software testing

Contact Info

Product

Resources

About