Subnets Generation of Program Nets and Its Application to Software Testing

Wu, Biao; Bao, Xiaoan; Zhang, Na; Morita, Hiromu; Nakata, Masao; Ge, Qi-Wei

doi:10.1587/transfun.e102.a.1303

Cited by 2 publications

(10 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we assume that all marked program nets are self-cleaning program nets with d 0 =0 as in our previous work [7]. We have designed algorithms to generate subnets (denoted by { P N i }) that can structurally cover all the nodes of a given program net [7]. These algorithms mainly carry out the following two steps: (1) making the given program net acyclic, and (2) generating subnets based on the acyclic program net.…”

Section: Program Netmentioning

confidence: 99%

“…In this section, we provide algorithms to generate constraint conditions for the choiceless program nets obtained in Ref. [7] by analyzing the nets.…”

Section: Constraint Conditions Genera-tionmentioning

confidence: 99%

“…We have structurally constructed a set of subnets that can cover all nodes for a given program net in our previous work [7]. The program nets have been represented around the structure so far without paying much attention to the detailed behavior of the nodes.…”

Section: Analysis Of Choiceless Program Netmentioning

confidence: 99%

“…Step (1) has been accomplished in our previous work [7] which can (i) treat a program as a program net, (ii) find and delete back edges to construct an acyclic program net, and (iii) generate a set of subnets that cover all of the nodes of the acyclic program net. This paper aims to do the remaining task (Step (2)), which is how to find test input data for each subnet.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, the expected outcome in this paper is to find a set of certain variable values for each obtained subnet under which all parts of the subnet can be executed. In a subnet obtained in our previous work [7], a SWITCH-node representing a switch operator is fixed only in one of the two states, T rue and F alse. Thus we call these subnets Choiceless Program Nets (Choiceless Nets for short) in this paper.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Test Input Data Generation for Choiceless Program Nets

2020

ECTI-CIT

Self Cite

View full text Add to dashboard Cite

Software testing is an important problem in designing a large software system and this problem is difficult to be solved due to its computational complexity. We try to use program nets to approach this problem and have proposed algorithms to divide a whole program net into subnets that can structurally cover the original one based on divide-and-conquer method. This paper aims to solve the remaining task of our approach, that is how to find test input data for each subnet to be called choiceless program net. Firstly, definitions of program nets are extended and the properties of choiceless program nets are analysed. Secondly, polynomial algorithms are proposed to get all constraint conditions of any given choiceless program nets. Finally, a method to generate test input data under the obtained constraint conditions is proposed by adopting an SMT (Satisfiability Modulo Theories) solver, $Z3$ prover, and also an example is given to show the usefulness of our method.

show abstract

Section: Program Netmentioning

confidence: 99%

“…In this section, we provide algorithms to generate constraint conditions for the choiceless program nets obtained in Ref. [7] by analyzing the nets.…”

Section: Constraint Conditions Genera-tionmentioning

confidence: 99%

Section: Analysis Of Choiceless Program Netmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Test Input Data Generation for Choiceless Program Nets

2020

ECTI-CIT

Self Cite

View full text Add to dashboard Cite

show abstract

Software Testing Integration-Based Model (I-BM) Framework for Recognizing Measure Fault Output Accuracy Using Machine Learning Approach

Zulkifli

Gaol

Trisetyarso

et al. 2023

Int. J. Soft. Eng. Knowl. Eng.

View full text Add to dashboard Cite

In software development, the software testing phase is an important process in determining the quality level of the software. Software testing is a process of executing a program aimed at finding errors in module access, units, and involves the execution of the system being tested on a number of test inputs, and determining whether the output produced is correct. In this study, a model-based testing (MBT) called integration-based model (I-BM) framework will be developed. This I-BM framework integrates testing variables from several software testing methods, namely black-box testing, white-box testing, unit testing, system testing, and acceptance testing. The integrated variables are function, interface, structure, performance, requirement, documentation, positives, and negatives. Then, this framework will document software errors to form a dataset, which will be measured for the level of accuracy of expected manual fault output using neural network algorithm and support vector machine. From the experiment results, it shows that the accuracy level of predicting fault output values from the I-BM framework using the neural network algorithm is on average 80%, and it produces a superior SVM architecture model in predicting I-BM framework output errors with an accuracy value of 0.99, precision of 0.99, recall of 0.99, and [Formula: see text]-score of 0.99. Compared to other MBT, the IBM framework has the advantage of being a more comprehensive software testing model because it starts from the identification of problems, analysis, design, documentation of software testing, and recommendations for each fault output found. Thus, software errors can be classified systematically in the form of a dataset, and not only focus on software testing for product lines and module mappings.

show abstract

Subnets Generation of Program Nets and Its Application to Software Testing

Cited by 2 publications

References 12 publications

Test Input Data Generation for Choiceless Program Nets

Test Input Data Generation for Choiceless Program Nets

Software Testing Integration-Based Model (I-BM) Framework for Recognizing Measure Fault Output Accuracy Using Machine Learning Approach

Contact Info

Product

Resources

About