Efficiently Finding Data Flow Subsumptions

Chaim, Marcos Lordello; Baral, Kesina; Offutt, Jeff; Concilio, Mario; Araujo, Roberto Paulo Andrioli de

doi:10.1109/icst49551.2021.00021

Cited by 9 publications

(11 citation statements)

References 30 publications

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“…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: 95%

“…This result has practical implications since SA allows to find the subsumption of DUAs by edges (local DUA-edge subsumption) and by DUAs (DUA-DUA subsumption) at [14], where N is the set of nodes of the program's flow graph and U is the set of DUAs required to test it according to all-uses criterion.…”

Section: Discussionmentioning

confidence: 99%

“…It models the problem of finding the local DUA-node subsumption; that is, those DUAs that are covered whenever a particular point p (e.g, a node) of a program is reached, as a data flow analysis framework [12], [13]. Using the local DUA-node subsumption, one can efficiently discover the subsumption of DUAs with respect to nodes, edges, and other DUAs [14].…”

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: 95%

Section: Discussionmentioning

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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“…The main idea is to restore the data flow in the business process by merging the calling diagram of external service and the resulting diagram of associated data operation into the control flow of the process model. Chaim et al [5] model data flow tests as a data flow analysis framework to quickly discover data flow relationships using efficient algorithms. Guo et al [7] proposed a new data perspective of workflow management and a mathematical technique to solve the problem of data exchange in business process centralization in a dynamic environment to better recover data flow.…”

Section: Related Workmentioning

confidence: 99%

“…Schneid et al [4] propose a method of using static code analysis tools to detect data flow in a simple business process without branches. The approach that writes rules through code analysis tools instead of human actions to find the data flow is partial, incoherent, cumbersome, and inaccurate when dealing with particularly complex BPM files [5]. We propose a new method to analyze data flow based on analyzing the reading and writing of data from logs of running processes.…”

Section: Introductionmentioning

confidence: 99%

Recovering Latent Data Flow from Business Process Model Automatically

Wang

Ali

et al. 2022

Wireless Communications and Mobile Computing

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Process-driven applications evolve rapidly through the interaction between executable BPMN (Business Process Modeling and Notation) models, business tasks, and external services. Given these components operate on some shared process data, it is imperative to recover the latent data by visiting relation, which is known as data flow among these tasks. Data flow will benefit some typical applications including data flow anomaly checking and data privacy protection. However, in most cases, the complete data flow in a business process is not explicitly defined but hidden in model elements such as form declarations, variable declarations, and program code. Some methods to recovering data flow based on process model analysis of source code have some drawbacks; i.e., for security reasons, users do not want to provide source code but only encapsulated methods; therefore, data flows are difficult to analyze. We propose a method to generate running logs that are used to produce a complete data flow picture combined with the static code analysis method. This method combines the simple and easy-to-use characteristics of static code analysis methods and makes up for the shortcomings of static code analysis methods that cannot adapt to complex business processes, and as a result, the analyzed data flow is inaccurate. Moreover, a holistic framework is proposed to generate the data flow graph. The prototype system designed on Camunda and Flowable BPM (business process management) engine proves the applicability of the solution. The effectiveness of our method is validated on the prototype system.

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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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Efficiently Finding Data Flow Subsumptions

Cited by 9 publications

References 30 publications

A Data Flow Analysis Framework for Data Flow Subsumption

A Data Flow Analysis Framework for Data Flow Subsumption

Recovering Latent Data Flow from Business Process Model Automatically

On subsumption relationships in data flow testing

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