An efficient bitwise algorithm for intra-procedural data-flow testing coverage

Chaim, Marcos Lordello; Araujo, Roberto Paulo Andrioli de

doi:10.1016/j.ipl.2013.02.007

Cited by 15 publications

(11 citation statements)

References 10 publications

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“…Wedyan et al [15] presented an approach for measuring data-flow coverage based on state variables defined in base classes or aspects in AspectJ programs. Chaim and Araujo [16] presented an approach to track intra-procedural definition use associations at run-time. The difference between our approach and theirs is that we are attempting to reduce monitoring costs by identifying a subset of DUPs to represent all DUPs in the program and design a novel fitness function that allows GA to generate a suitable test case at a time.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary approach to generating test data for data flow test

et al. 2018

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Software testing consumes a significant portion of software effort. Program entities such as branch or definition-use pairs (DUPs) are used in diverse software development tasks. In this study, the authors present a novel evolution-based approach to generating test data for all definition-use coverage. First, the subset of DUPs, which can ensure the coverage adequacy, is computed by a reduction algorithm for the whole DUPs. Then they apply a genetic algorithm to generate test data for the subset of DUPs. Furthermore, the fitness of an individual depends on the matching degree between the traversed path and the definition-clear path of each target DUP. They also investigate the coverage and the size of test cases of test data generation by applying the authors' approach on 15 widely used subject programs. The experimental results show that their approach can reduce the size of test cases that generated without affecting the coverage rate.

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Section: Related Workmentioning

confidence: 99%

Evolutionary approach to generating test data for data flow test

et al. 2018

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“…Figure 1 presents a program that finds the maximum element in an array of integers [15] and Figure 2 presents its control flow graph. The numbers at the start of each line of code in Figure 1 indicate the line's corresponding node in the graph.…”

Section: Introductionmentioning

confidence: 99%

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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“…Previous studies show that statements and branches can be monitored with a run-time overhead of 9% to 18% while DUAs have a runtime overhead of 66%-127% [18], [19]. Recent results, though, show that DUA spectra can be obtained with fairly moderate execution and memory overheads [20], [21]. They allow the use of data-flow spectra in fault localization for large and long running programs.…”

Section: Introductionmentioning

confidence: 99%

“…In what follows, we present an example to illustrate the concepts of control-and data-flow spectra, and spectrum-based fault localization. Figure 1 shows the code of a simple function, named max [20]. The first three columns represent line, statement, and block numbers, respectively.…”

Section: Introductionmentioning

confidence: 99%

Evaluating data-flow coverage in spectrum-based fault localization

Ribeiro

Araujo

Chaim

et al. 2019

2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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Background: Debugging is a key task during the software development cycle. Spectrum-based Fault Localization (SFL) is a promising technique to improve and automate debugging. SFL techniques use control-flow spectra to pinpoint the most suspicious program elements. However, data-flow spectra provide more detailed information about the program execution, which may be useful for fault localization. Aims: We evaluate the effectiveness and efficiency of ten SFL ranking metrics using data-flow spectra. Method: We compare the performance of data-and control-flow spectra for SFL using 163 faults from 5 real-world open source programs, which contain from 468 to 4130 test cases. The data-and control-flow spectra types used in our evaluation are definition-use associations (DUAs) and lines, respectively. Results: Using data-flow spectra, up to 50% more faults are ranked in the top-15 positions compared to control-flow spectra. Also, most SFL ranking metrics present better effectiveness using data-flow to inspect up to the top-40 positions. The execution cost of data-flow spectra is higher than control-flow, taking from 22 seconds to less than 9 minutes. Dataflow has an average overhead of 353% for all programs, while the average overhead for control-flow is of 102%. Conclusions: The results suggest that SFL techniques can benefit from using data-flow spectra to classify faults in better positions, which may lead developers to inspect less code to find bugs. The execution cost to gather data-flow is higher compared to control-flow, but it is not prohibitive. Moreover, data-flow spectra also provide information about suspicious variables for fault localization, which may improve the developers' performance using SFL.

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An efficient bitwise algorithm for intra-procedural data-flow testing coverage

Cited by 15 publications

References 10 publications

Evolutionary approach to generating test data for data flow test

Evolutionary approach to generating test data for data flow test

A Data Flow Analysis Framework for Data Flow Subsumption

Evaluating data-flow coverage in spectrum-based fault localization

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