Two notions of correctness and their relation to testing

Budd, Timothy A.; Angluin, Dana

doi:10.1007/bf00625279

Cited by 213 publications

(127 citation statements)

References 24 publications

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“…This enables the flexible implementation of the loop, first explored by Weyuker [21] and Budd and Angluin [3], of linking model inference to test generation. We provide an instantiation of this framework that shows how the WEKA implementation fo the C4.5 algorithm [12] can be linked up to Z3 [5] to generate test sets for three programs.…”

Section: Discussionmentioning

confidence: 99%

“…Until the mid-nineties this work was predominantly theoretical [3], [21], [4], [15], [14], [23], [22]. This developed strong theoretical underpinnings for test adequacy in an inference context (by linking to Machine-Learning notions such as Probably Approximately Correct learning, for example [23], [18]).…”

Section: Related Workmentioning

confidence: 99%

“…In the early eighties, Weyuker [21] and Budd and Angluin [3] examined this problem from the perspective of Machine Learning. They advanced the argument that testing and model inference are, in effect, two sides of the same coin.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Black-Box Test Generation from Inferred Models

Papadopoulos

Walkinshaw

2015

2015 IEEE/ACM 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering

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Abstract-Automatically generating test inputs for components without source code (are 'black-box') and specification is challenging. One particularly interesting solution to this problem is to use Machine Learning algorithms to infer testable models from program executions in an iterative cycle. Although the idea has been around for over 30 years, there is little empirical information to inform the choice of suitable learning algorithms, or to show how good the resulting test sets are. This paper presents an openly available framework to facilitate experimentation in this area, and provides a proof-of-concept inference-driven testing framework, along with evidence of the efficacy of its test sets on three programs.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Black-Box Test Generation from Inferred Models

Papadopoulos

Walkinshaw

2015

2015 IEEE/ACM 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering

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“…These mutants are called equivalent mutants and need to be identified and eliminated if possible. Budd and Angluin [9] proved that detecting equivalent mutants is an undecidable problem.…”

Section: Introductionmentioning

confidence: 99%

An Approach for the Generation of Higher Order Mutants Using Genetic Algorithms

Abuljadayel¹,

Wedyan²

2018

IJISA

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Abstract-Mutation testing is a structural testing technique in which the effectiveness of a test suite is measured by the suite ability to detect seeded faults. One fault is seeded into a copy of the program, called mutant, leading to a large number of mutants with a high cost of compiling and running the test suite against the mutants. Moreover, many of the mutants produce the same output as the original program (called equivalent mutants), such mutants need to be minimized to produce accurate results. Higher order mutation testing aims at solving these problems by allowing more than one fault to be seeded in the mutant. Recent work in higher order mutation show promising result in reducing the cost of mutation testing and increasing the approach effectiveness. In this paper, we present an approach for generating higher order mutants using a genetic algorithm. The aim of the proposed approach is to produce subtle and harder to kill mutants, and reduce the percentage of produced equivalent mutants. A Java tool has been developed, called HOMJava (Higher Order Mutation for Java), which implements the proposed approach. An experimental study was performed to evaluate the effectiveness of the proposed approach. The results show that the approach was able to produce subtle higher order mutants, the fitness of mutants improved by almost 99% compared with the first order mutants used in the experiment. The percentage of produced equivalent mutants was about 4%.

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“…Despite the fact that deducing mutant equivalence is undecidable [4], this has motivated some research into how the problem can be circumvented [11]. For example, let S o (I) and S m (I) denote the respective outputs of S o and S m for a given input.…”

Section: Introductionmentioning

confidence: 99%

Resolving the Equivalent Mutant Problem in the Presence of Non-determinism and Coincidental Correctness

Patel

Hierons

2016

Testing Software and Systems

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Abstract. In this paper, we develop a new mutation testing technique called Interlocutory Mutation Testing (IMT) that mitigates the equivalent mutant problem in the presence of coincidental correctness and non-determinism. The accuracy of IMT was evaluated; it obtained a classification accuracy of 93.33% for non-equivalent mutants and 100% for equivalent mutants in a non-deterministic system with coincidental correctness.

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Two notions of correctness and their relation to testing

Cited by 213 publications

References 24 publications

Black-Box Test Generation from Inferred Models

Black-Box Test Generation from Inferred Models

An Approach for the Generation of Higher Order Mutants Using Genetic Algorithms

Resolving the Equivalent Mutant Problem in the Presence of Non-determinism and Coincidental Correctness

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