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

2020 ◽  
Vol 10 (10) ◽  
pp. 3397 ◽  
Author(s):  
Aman Jaffari ◽  
Cheol-Jung Yoo ◽  
Jihyun Lee
Keyword(s):  
Fault Detection ◽  
Test Data ◽  
Large Set ◽  
Generation Process ◽  
Test Data Generation ◽  
Test Automation ◽  
Data Generation ◽  
Activity Diagram ◽  
Coverage Criterion ◽  
Better Than

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.

AUTOMATIC PRODUCTION OF TEST DATA WITH A MULTIPLE BATCH-OPTIMISTIC METHOD

2009 ◽  
Vol 18 (01) ◽  
pp. 61-80 ◽  
Author(s):  
ANASTASIS A. SOFOKLEOUS ◽  
ANDREAS S. ANDREOU
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Test Data ◽  
Automatic Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
Generation System ◽  
Automatic Production ◽  
Coverage Criterion ◽  
Testing Coverage

Recent research on software testing focuses on integrating techniques, such as computational intelligence, with special purpose software tools so as to minimize human effort, reduce costs and automate the testing process. This work proposes a complete software testing framework that utilizes a series of specially designed genetic algorithms to generate automatically test data with reference to the edge/condition testing coverage criterion. The framework utilizes a program analyzer, which examines the program's source code and builds dynamically program models for automatic testing, and a test data generation system that utilizes genetic algorithms to search the input space and determine a near to optimum set of test cases with respect to the testing coverage criterion. The performance of the framework is evaluated on a pool of programs consisting of both standard and random-generated programs. Finally, the proposed test data generation system is compared against other similar approaches and the results are discussed.

scholarly journals Survey on Mutation-based Test Data Generation

2015 ◽  
Vol 5 (5) ◽  
pp. 1164
Author(s):  
Hanh Le Thi My ◽  
Binh Nguyen Thanh ◽  
Tung Khuat Thanh
Keyword(s):  
Test Data ◽  
Test Cases ◽  
Generation Process ◽  
Test Data Generation ◽  
Data Generation ◽  
Comprehensive Survey ◽  
Critical Activity ◽  
Automatic Test Data Generation ◽  
Systematic Selection ◽  
Selection Of

<span>The critical activity of testing is the systematic selection of suitable test cases, which be able to reveal highly the faults. Therefore, mutation coverage is an effective criterion for generating test data. Since the test data generation process is very labor intensive, time-consuming and error-prone when done manually, the automation of this process is highly aspired. The researches about automatic test data generation contributed a set of tools, approaches, development and empirical results. In this paper, we will analyse and conduct a comprehensive survey on generating test data based on mutation. The paper also analyses the trends in this field.</span>

scholarly journals IMPROVING THE RELIABILITY AND VALIDITY OF TEST DATA ADEQUACY IN PROGRAMMING ASSESSMENTS

2015 ◽  
Vol 77 (9) ◽  
Author(s):  
Rohaida Romli ◽  
Shahida Sulaiman ◽  
Kamal Zuhairi Zamli
Keyword(s):  
Test Data ◽  
Dynamic Testing ◽  
Reliability And Validity ◽  
Structural Testing ◽  
Functional Testing ◽  
Generation Process ◽  
Test Data Generation ◽  
Data Generation ◽  
Data Adequacy ◽  
Programming Courses

Automatic Programming Assessment (or APA) has recently become a notable method in assisting educators of programming courses to automatically assess and grade students’ programming exercises as its counterpart; the typical manual tasks are prone to errors and lead to inconsistency. Practically, this method also provides an alternative means of reducing the educators’ workload effectively. By default, test data generation process plays an important role to perform a dynamic testing on students’ programs. Dynamic testing involves the execution of a program against different inputs or test data and the comparison of the results with the expected output, which must conform to the program specifications. In the software testing field, there have been diverse automated methods for test data generation. Unfortunately, APA rarely adopts these methods. Limited studies have attempted to integrate APA and test data generation to include more useful features and to provide a precise and thorough quality program testing. Thus, we propose a framework of test data generation known as FaSt-Gen covering both the functional and structural testing of a program for APA. Functional testing is a testing that relies on specified functional requirements and focuses the output generated in response to the selected test data and execution, Meanwhile, structural testing looks at the specific program logic to verify how it works. Overall, FaSt-Gen contributes as a means to educators of programming courses to furnish an adequate set of test data to assess students’ programming solutions regardless of having the optimal expertise in the particular knowledge of test cases design. FaSt-Gen integrates the positive and negative testing criteria or so-called reliable and valid test adequacy criteria to derive desired test data and test set schema. As for the functional testing, the integration of specification-derived test and simplified boundary value analysis techniques covering both the criteria. Path coverage criterion guides the test data selection for structural testing. The findings from the conducted controlled experiment and comparative study evaluation show that FaSt-Gen improves the reliability and validity of test data adequacy in programming assessments.

scholarly journals An Analysis on the Applicability of Meta-Heuristic Searching Techniques for Automated Test Data Generation in Automatic Programming Assessment

2019 ◽  
Vol 16 (2(SI)) ◽  
pp. 0515 ◽  
Author(s):  
Musa Et al.
Keyword(s):  
Test Data ◽  
Comparative Evaluation ◽  
Dynamic Testing ◽  
Cuckoo Search ◽  
Automatic Programming ◽  
Generation Process ◽  
Test Data Generation ◽  
Data Generation ◽  
Automated Test ◽  
Automated Test Data Generation

Automatic Programming Assessment (APA) has been gaining lots of attention among researchers mainly to support automated grading and marking of students’ programming assignments or exercises systematically. APA is commonly identified as a method that can enhance accuracy, efficiency and consistency as well as providing instant feedback on students’ programming solutions. In achieving APA, test data generation process is very important so as to perform a dynamic testing on students’ assignment. In software testing field, many researches that focus on test data generation have demonstrated the successful of adoption of Meta-Heuristic Search Techniques (MHST) so as to enhance the procedure of deriving adequate test data for efficient testing. Nonetheless, thus far the researches on APA have not yet usefully exploited the techniques accordingly to include a better quality program testing coverage. Therefore, this study has conducted a comparative evaluation to identify any applicable MHST to support efficient Automated Test Data Generation (ATDG) in executing a dynamic-functional testing in APA. Several recent MHST are included in the comparative evaluation combining both the local and global search algorithms ranging from the year of 2000 until 2018. Result of this study suggests that the hybridization of Cuckoo Search with Tabu Search and lévy flight as one of promising MHST to be applied, as it’s outperforms other MHST with regards to number of iterations and range of inputs.

Application of modified differential evolution in test data generation

2009 ◽  
Vol 29 (6) ◽  
pp. 1722-1724
Author(s):  
Xiao-cheng HUANG ◽  
Xi-wu WANG ◽  
Dong-sheng CHANG ◽  
Gang HE
Keyword(s):  
Differential Evolution ◽  
Test Data ◽  
Test Data Generation ◽  
Data Generation
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