Automatic Test Data Generation Using the Activity Diagram and Search-Based Technique

Aman Jaffari,Cheol-Jung Yoo,Jihyun Lee

doi:10.3390/app10103397

Abstract

In software testing, generating test data is quite expensive and time-consuming. The manual generation of an appropriately large set of test data to satisfy a specified coverage criterion carries a high cost and requires significant human effort. Currently, test automation has come at the cost of low quality. In this paper, we are motivated to propose a model-based approach utilizing the activity diagram of the system under test as a test base, focusing on its data flow aspect. The technique is incorporated with a search-based optimization heuristic to fully automate the test data generation process and deliver test cases with more improved quality. Our experimental investigation used three open-source software systems to assess and compare the proposed technique with two alternative approaches. The experimental results indicate the improved fault-detection performance of the proposed technique, which was 11.1% better than DFAAD and 38.4% better than EvoSuite, although the techniques did not differ significantly in terms of statement and branch coverage. The proposed technique was able to detect more computation-related faults and tends to have better fault detection capability as the system complexity increases.

Highlights

Test case design is an important activity that consumes a large share of the budget and effort required for software testing
RQ1: How do the tests generated by the proposed AutoTDGen perform, compared with alternative approaches, in terms of statement and branch coverage? The results presented in Table 3 for statement coverage (SC) indicate that AutoTDGen performed slightly better than data flow annotated activity diagram (DFAAD) and EvoSuite on two subjects
In terms of branch coverage (BC), AutoTDGen achieved greater BC than EvoSuite in only a single subject (Elevator), but it outperformed DFAAD in all subjects

Summary

Introduction

Test case design is an important activity that consumes a large share of the budget and effort required for software testing. Generating test data for executable test cases is a major challenge [1]. Building a system to generate test data that maximizes fault detection effectiveness and minimizes the cost and effort is essential. The use of software models to automate the test generation process and reduce the testing cost and effort has been an active area of research for a long time [2,3,4]. Interest in model-based test automation has expanded along with the acceptance of Unified Modeling Language (UML) diagrams as the de facto standard for modeling software systems. Recent advances in model-based testing (MBT) have increased the feasibility and effectiveness of using

Results

Discussion

Conclusion