A New Approach for Search Space Reduction and Seeding by Analysis of the Clauses

Bidgoli, Atieh Monemi; Haghighi, Hassan

doi:10.1007/978-3-319-99241-9_19

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Previous work has also shown that extracting and directly seeding the constant values from source code of program can significantly improve the structural coverage testing [4,14,24,52], particular for programs heavily relying on constant values. However, due to the nested nature of target programs, the impact of the conventional seeding approach is prominent only in the earlier phases of search process.…”

Section: Sbst Techniques Benefitting From Seedingmentioning

confidence: 99%

Seeding Grammars in Grammatical Evolution to Improve Search-Based Software Testing

Anjum

Ryan

2021

SN COMPUT. SCI.

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Heuristic-based optimization techniques have been increasingly used to automate different types of code coverage analysis. Several studies suggest that interdependencies (in the form of comparisons) may exist between the condition constructs, of variables and constant values, in the branching conditions of real-world programs, e.g. ($$i \le 100$$ i ≤ 100 ) or ($$i==j$$ i = = j ), etc. In this work, by interdependencies we refer to the situations where, to satisfy a branching condition, there must be a certain relationship between the values of some specific condition constructs (which may or may not be a part of the respective condition predicates). For example, the values of variables i and j must be equal to satisfy the condition of ($$i==j$$ i = = j ), and the value of variable k must be equal to 100 for the satisfaction of the condition of ($$k==100$$ k = = 100 ). To date, only the Ariadne, a Grammatical Evolution (GE)-based system, exploits these interdependencies between input variables (e.g. of the form ($$i \le j$$ i ≤ j ) or ($$i==j$$ i = = j ), etc.) to efficiently generate test data. Ariadne employs a simple attribute grammar to exploit these dependencies, which enables it to evolve complex test data, and has been compared favourably to other well-known techniques in the literature. However, Ariadne does not benefit from interdependencies involving constants, e.g. ($$i \le 100$$ i ≤ 100 ) or ($$j==500$$ j = = 500 ), etc., due to the difficulty in evolving precise values, and these are equally important constructs of condition predicates. Furthermore, constant creation in GE can be difficult, particularly with high precision. We propose to seed the grammar with constants extracted from the source code of the program under test to enhance and extend Ariadne’s capability to exploit richer types of dependencies (involving all combinations of both variables and constant values). We compared our results with the original system of Ariadne against a large set of benchmark problems which include 10 numeric programs in addition to the ones originally used for Ariadne. Our results demonstrate that the seeding strategy not only dramatically improves the generality of the system, as it improves the code coverage (effectiveness) by impressive margins, but it also reduces the search budgets (efficiency) often up to an order of magnitude. Moreover, we also performed a rigorous analysis to investigate the scalability of our improved Ariadne, showing that it stays highly scalable when compared to both the original system of Ariadne and GA-based test data generation approach.

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Section: Sbst Techniques Benefitting From Seedingmentioning

confidence: 99%

Seeding Grammars in Grammatical Evolution to Improve Search-Based Software Testing

Anjum

Ryan

2021

SN COMPUT. SCI.

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Search‐Based MC/DC Test Data Generation With OCL Constraints

Sartaj,

Zohaib Iqbal,

Ahmed Jilani

et al. 2024

Software Testing Verif & Rel

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System‐level testing of avionics software systems requires compliance with different international safety standards such as DO‐178C. An important consideration of the avionics industry is automated test data generation according to the criteria suggested by safety standards. One of the recommended criteria by DO‐178C is the modified condition/decision coverage (MC/DC) criterion. Current model‐based test data generation approaches use constraints written in Object Constraint Language (OCL) and apply search techniques to generate test data. These approaches either do not support MC/DC criterion or suffer from performance issues while generating test data for large‐scale avionics systems. In this paper, we propose an effective way to automate MC/DC test data generation during model‐based testing. We develop a strategy that utilizes case‐based reasoning (CBR) and range reduction heuristics designed to solve MC/DC‐tailored OCL constraints. We performed an empirical study to compare our proposed strategy for MC/DC test data generation using CBR, range reduction, both CBR and range reduction, with an original search algorithm, and random search. We also empirically compared our strategy with existing constraint‐solving approaches. The results show that both CBR and range reduction for MC/DC test data generation outperform the baseline approach. Moreover, the combination of both CBR and range reduction for MC/DC test data generation is an effective approach compared to existing constraint solvers.

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Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing

Anjum

Ryan

2020

Lecture Notes in Computer Science

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A New Approach for Search Space Reduction and Seeding by Analysis of the Clauses

Cited by 3 publications

References 7 publications

Seeding Grammars in Grammatical Evolution to Improve Search-Based Software Testing

Seeding Grammars in Grammatical Evolution to Improve Search-Based Software Testing

Search‐Based MC/DC Test Data Generation With OCL Constraints

Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing

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