Evolutionary approach to generating test data for data flow test

Jiang, Shujuan; Chen, Jieqiong; Zhang, Yanmei; Qian, Junyan; Wang, Rongcun; Xue, Meng

doi:10.1049/iet-sen.2018.5197

Cited by 11 publications

(26 citation statements)

References 23 publications

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“…Santelices and Harrold [11] propose an algorithm to find DUAedge subsumption at O(| U |) cost. Marré and Bertolino's [9], [21], [22] and Jiang et al's [10] solutions are quadratic to the number of DUAs for the DUA-DUA subsumption, which hamper their application at industrial settings. Experimental results suggest that SA's application on industry-like applications works at scale and is quite promising [14].…”

Section: Discussionmentioning

confidence: 99%

“…Node n j post-dominates node n i if any path from n j to the exit node includes n i . A node dominates itself but does not post-dominate itself [10]. In the absence of early program termination, node 5 post-dominates nodes 4, 1, 2, and 6.…”

Section: Dua-node Subsumptionmentioning

confidence: 99%

“…Many approaches to reduce DFT's cost have been created. Some exploit the subsumption relationship among DUAs [9], [10]. A test requirement (TR) tr 1 (e.g., a DUA D 1 ) subsumes another test requirement tr 2 (another DUA D 2 ) if every complete path that traverses tr 1 also traverses tr 2 .…”

Section: Introductionmentioning

confidence: 99%

“…If a spanning set of DUAs could be identified, testers would only need to satisfy the unconstrained DUAs, saving time and effort. Jiang et al [10] showed that targeting unconstrained DUAs reduces the cost of input data generation. Additionally, DUAs can be subsumed by test requirement (e.g., node, edge) of different criteria so that once they are covered some DUAs are guaranteed to also be covered [11].…”

Section: Introductionmentioning

confidence: 99%

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A Data Flow Analysis Framework for Data Flow Subsumption

Chaim,

Baral,

Offutt

2021

Preprint

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Data flow testing creates test requirements as definition-use (DU) associations, where a definition is a program location that assigns a value to a variable and a use is a location where that value is accessed. Data flow testing is expensive, largely because of the number of test requirements. Luckily, many DU-associations are redundant in the sense that if one test requirement (e.g., node, edge, DU-association) is covered, other DU-associations are guaranteed to also be covered. This relationship is called subsumption. Thus, testers can save resources by only covering DU-associations that are not subsumed by other testing requirements. In this work, we formally describe the Data Flow Subsumption Framework (DSF) conceived to tackle the data flow subsumption problem. We show that DFS is a distributive data flow analysis framework which allows efficient iterative algorithms to find the Meet-Over-All-Paths (MOP) solution for DSF transfer functions. The MOP solution implies that the results at a point p are valid for all paths that reach p. We also present an algorithm, called Subsumption Algorithm (SA), that uses DSF transfer functions and iterative algorithms to find the local DUassociations-node subsumption; that is, the set of DU-associations that are covered whenever a node n is toured by a test. A proof of SA's correctness is presented and its complexity is analyzed.

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Section: Discussionmentioning

confidence: 99%

Section: Dua-node Subsumptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Data Flow Analysis Framework for Data Flow Subsumption

Chaim,

Baral,

Offutt

2021

Preprint

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“…This testing adequacy criterion is represented in the form of fitness function [5]. In automatic generation of test cases most difficult task is the selection of test adequacy criterion from various criteria available in the literature [6].…”

Section: Introductionmentioning

confidence: 99%

Test Case Generation for Data Flow Testing using Cuckoo Search Algorithm

2019

IJRTE

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Software testing consumes the major portion of the total efforts required for software development. This activity is very time consuming and labor intensive. It is very hard to do testing in optimal manner. In this paper a new approach is proposed, which uses the nature inspired stochastic algorithm called Cuckoo Search Algorithm (CSA) for the automatic generation of test data for data flow testing. This approach considers all def-use as test adequacy criteria. For assistance to CSA in the state space a new fitness function is also proposed by using the concept of dominator tree and branch distance in a CFG. To validate the proposed approach experiments are carried out on 10 benchmarked programs and findings are contrasted with earlier work done in this domain. Further in order to prove that proposed approach performs better than the above mentioned approaches a statistical difference test (T-test) is also performed.

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On subsumption relationships in data flow testing

Chaim

Baral

Offutt

et al. 2023

Software Testing Verif & Rel

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SummaryData flow testing creates test requirements as definition‐use (DU) associations, where a definition is a program location that assigns a value to a variable and a use is a location where that value is accessed. Data flow testing is expensive, largely because of the number of test requirements. Luckily, many DU‐associations are redundant in the sense that if one test requirement (e.g. node, edge and DU‐association) is covered, other DU‐associations are guaranteed to also be covered. This relationship is called subsumption. Thus, testers can save resources by only covering DU‐associations that are not subsumed by other testing requirements. Although this has the potential to significantly decrease the cost of data flow testing, there are roadblocks to its application. Finding data flow subsumptions correctly and efficiently has been an elusive goal; the savings provided by data flow subsumptions and the cost to find them need to be assessed; and the fault detection ability of a reduced set of DU‐associations and the advantages of data flow testing over node and edge coverage need to be verified. This paper presents novel solutions to these problems. We present algorithms that correctly find data flow subsumptions and are asymptotically less costly than previous algorithms. We present empirical data that show that data flow subsumption is effective at reducing the number of DU‐associations to be tested and can be found at scale. Furthermore, we found that using reduced DU‐associations decreased the fault detection ability by less than 2%, and data flow testing adds testing value beyond node and edge coverage.

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Evolutionary approach to generating test data for data flow test

Cited by 11 publications

References 23 publications

A Data Flow Analysis Framework for Data Flow Subsumption

A Data Flow Analysis Framework for Data Flow Subsumption

Test Case Generation for Data Flow Testing using Cuckoo Search Algorithm

On subsumption relationships in data flow testing

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