Scaling Test Case Generation For Expressive Decision Tables

Agrawal, Supriya; Venkatesh, R.; Shrotri, Ulka; Zare, Amey; Verma, Sagar

doi:10.1109/icst46399.2020.00044

Cited by 3 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the initially intended purpose of cause-effect graphs for testing hardware logical circuits in [1], truth tables of logical relations are very similar to those of logical gates. In addition to the four standard logical relations: DIR (direct), NOT (negation), AND (conjunction) and OR (disjunction), two additional relations (which are omitted in some works such as [4] and [19]) can be used: NAND (Peirce arrow) and NOR (Sheffer stroke). The direct and negation logical relations are unary, meaning they have exactly one cause and one effect node, whereas all other logical relations are n-ary, meaning they have n > 1 causes and one effect node.…”

Section: A Cause-effect Graph Specification Elementsmentioning

confidence: 99%

New Graphical Software Tool for Creating Cause-Effect Graph Specifications

Krupalija

Bećirović²,

Prazina

et al. 2022

JCOMSS

View full text Add to dashboard Cite

Cause-effect graphing is a commonly used black-box technique with many applications in practice. It is important to be able to create accurate cause-effect graph specifications from system requirements before converting them to test case tables used for black-box testing. In this paper, a new graphical software tool for creating cause-effect graph specifications is presented. The tool uses standardized graphical notation for describing different types of nodes, logical relations and constraints, resulting in a visual representation of the desired cause-effect graph which can be exported for later usage and imported in the tool. The purpose of this work is to make the cause-effect graph specification process easier for users in order to solve some of the problems which arise due to the insufficient amount of understanding of cause-effect graph elements. The proposed tool was successfully used for creating cause-effect graph specifications for small, medium and large graphs. It was also successfully used for performing different types of tasks by users without any prior knowledge of the functionalities of the tool, indicating that the tool is easy to use, helpful and intuitive. The results indicate that the usage of standardized notation is easier to understand than nonstandardized approaches from other tools. Index terms-software quality, black-box testing, cause-effect graph, graphical software tool.

show abstract

Section: A Cause-effect Graph Specification Elementsmentioning

confidence: 99%

New Graphical Software Tool for Creating Cause-Effect Graph Specifications

Krupalija

Bećirović²,

Prazina

et al. 2022

JCOMSS

View full text Add to dashboard Cite

show abstract

“…Recently, algorithms such as Ant-colony-based Generation of Test cases (AGenT) [29] have been developed for the purpose of automatic functional test generation from formal system requirements. Environment modeling was proposed in [30] for black-box testing automation of real-time embedded systems which have safety and time constraints without any knowledge of the system design, whereas data mining techniques have successfully been applied in [31] for converting system requirements to cause-effect graph specifications.…”

Section: Figure 1 Diagram That Describes the Process Of Choosing The ...mentioning

confidence: 99%

Forward-Propagation Approach for Generating Feasible and Minimum Test Case Suites From Cause-Effect Graph Specifications

et al. 2022

View full text Add to dashboard Cite

Cause-effect graphs are a popular black-box testing technique. The most commonly used approach for generating test cases from cause-effect graph specifications uses backward propagation of forced effect activations through the graph in order to get the values of causes for the desired test case. Many drawbacks have been identified when using this approach for different testing requirements. Several algorithms for automatically generating test case suites from cause-effect graph specifications have been proposed. However, many of these algorithms do not solve the main drawbacks of the initial backpropagation approach and offer only minor improvements for specific purposes. This work proposes two new algorithms for deriving test cases from cause-effect representationsan algorithm that uses forwardpropagation of all possible cause values in order to get all feasible test cases and an algorithm that minimizes the feasible test case suite while taking multiple effect activations into account. Cause-effect graphs previously used for testing algorithms based on the backward-propagation approach were used for evaluation and comparison, as well as the newly introduced test effect coverage metric and fault detection rate effectiveness. The evaluation shows that the proposed algorithms work in real time even for a very large number of cause nodes. The results also indicate that the first newly proposed algorithm generates a larger test case suite, whereas the second newly proposed algorithm generates a smaller test case subset than the originally proposed approaches while ensuring the maximum effect coverage, fault detection rate effectiveness and a better test effect coverage ratio.INDEX TERMS Black-box testing, cause-effect graph, forward-propagation algorithm, software quality, test suite generation.

show abstract