Multifactor Algorithm for Test Case Selection and Ordering

Main Article Content

Atulya Gupta
Rajendra Prasad Mahapatra

Abstract

Regression testing being expensive, requires optimization notion. Typically, the optimization of test cases results in selecting a reduced set or subset of test cases or prioritizing the test cases to detect potential faults at an earlier phase. Many former studies revealed the heuristic-dependent mechanism to attain optimality while reducing or prioritizing test cases. Nevertheless, those studies were deprived of systematic procedures to manage tied test cases issue. Moreover, evolutionary algorithms such as the genetic process often help in depleting test cases, together with a concurrent decrease in computational runtime. However, when examining the fault detection capacity along with other parameters, is required, the method falls short. The current research is motivated by this concept and proposes a multifactor algorithm incorporated with genetic operators and powerful features. A factor-based prioritizer is introduced for proper handling of tied test cases that emerged while implementing re-ordering. Besides this, a Cost-based Fine Tuner (CFT) is embedded in the study to reveal the stable test cases for processing. The effectiveness of the outcome procured through the proposed minimization approach is anatomized and compared with a specific heuristic method (rule-based) and standard genetic methodology. Intra-validation for the result achieved from the reduction procedure is performed graphically. This study contrasts randomly generated sequences with procured re-ordered test sequence for over '10' benchmark codes for the proposed prioritization scheme. Experimental analysis divulged that the proposed system significantly managed to achieve a reduction of 35-40% in testing effort by identifying and executing stable and coverage efficacious test cases at an earlier phase.

Downloads

Download data is not yet available.

Article Details

How to Cite
1.
Gupta A, Mahapatra RP. Multifactor Algorithm for Test Case Selection and Ordering. Baghdad Sci.J [Internet]. [cited 2021Jun.16];18(2(Suppl.):1056. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5517
Section
article

References

Pradhan D, Wang S, Ali S, Yue T, Liaaen M. REMAP: Using Rule Mining and Multi-Objective Search for Dynamic Test Case Prioritization. In 2018 IEEE 11th ICST; 2018; Vasteras. p. 46-57.

Gupta N, Sharma A, Pachariya MK. An Insight Into Test Case Optimization: Ideas and Trends With Future Perspectives. IEEE Access. 2019; 7: 22310-22327.

Alian M, Suleiman D, Shaout A. Test Case Reduction Techniques - Survey. Int J Adv Comput Sci Appl. 2016 Jun; 7(5): 264-275.

Mohapatra SK, Pradhan M. Finding Representative Test Suit for Test Case Reduction in Regression Technique. In 2015 IEEE IC4; 2015; Indore. p. 1-6.

Harris P, Raju N. A Greedy Approach for Coverage-Based Test Suite Reduction. Int Arab J Inf Technol. 2015 Jan; 12(1): 17-23.

Lin CT, Tang KW, Wang JS, Kapfhammer GM. Empirically evaluating Greedy-based test suite reduction methods at different levels of test suite complexity. Sci Comput Program. 2017 Dec; 150: 1-25.

Vahabzadeh A, Stocco A, Mesbah A. Fine-Grained Test Minimization. In 2018 ACM/IEEE 40th Int Conf Softw Eng; 2018; Gothenburg. p. 210- 221.

Jeffrey D, Gupta N. Improving Fault Detection Capability by Selectively Retaining Test Cases during Test Suite Reduction. IEEE Trans Softw Eng. 2007 Feb; 33(2): 108-123.

Agrawal AP, Choudhary A, Kaur A, Pandey HM. Fault coverage-based test suite optimization method for regression testing: learning from mistakes-based approach. Neural Comput Appl. 2020 Jun; 32: 7769–7784.

Singh L, Singh SN, Dawra S, Tuli R. A New Technique for Test Suite Minimization in Regression Testing. SSRN Electron J. 2019 Jan.

Lawanna A. Test case design based technique for the improvement of test case selection in software maintenance. In 2016 55th Annu Conf SICE Jpn; 2016; Tsukuba. p. 345-350.

Lawanna A. Filtering test case selection for increasing the performance of regression testing. Int J Appl Sci Technol. 2016; 9(1): 19-25.

Panda S, Mohapatra DP. Regression test suite minimization using integer linear programming model. Softw Pract Exp. 2017 Nov; 47(11): 1539-1560.

Kazmi R, Jawawi DNA, Mohamad R, Ghani I, Younas M. A Test Case Selection Framework and Technique: Weighted Average Scoring Method. J Telecommun Electron Comput Eng. 2017; 9: 15-22.

Marchetto A, Scanniello G, Susi A. Combining Code and Requirements Coverage with Execution Cost for Test Suite Reduction. IEEE Trans Softw Eng. 2019 Apr; 45(4): 363-390.

Mukherjee R, Patnaik KS. A Survey on Different Approaches for Software Test Case Prioritization. J King Saud Univ - Comput Inf Sci. 2018 Oct.

Beena R, Sarala S. Code coverage based test case selection and prioritization. Int J Softw Eng Appl. 2013; 4(6): 39-49.

Zhou J, Hao D. Impact of Static and Dynamic Coverage on Test-Case Prioritization: An Empirical Study. In 2017 IEEE ICST Workshops; 2017; Tokyo. p. 392-394.

Mirarab S, Tahvildari L. A Prioritization Approach for Software Test Cases Based on Bayesian Networks. Fundam Approaches Softw Eng. 2007; 4422: 276-290.

Carlson R, Do H, Denton A. A clustering approach to improving test case prioritization: An industrial case study. In 2011 27th IEEE ICSM; 2011; Williamsburg. p. 382-391.

Zhao X, Wang Z, Fan X, Wang Z. A Clustering-Bayesian Network Based Approach for Test Case Prioritization. In 2015 IEEE 39th Annu Int Comput Softw Appl Conf; 2015; Taichung. p. 542-547.

Mahmood MH, Hosain MS. Improving Test Case Prioritization Based on Practical Priority Factors. In 2017 8th IEEE ICSESS; 2017; Beijing. p. 899-902.

Khalilian A, Azgomi MA, Fazlalizadeh Y. An improved method for test case prioritization by incorporating historical test case data. Sci Comput Program. 2012; 78(1): 93-116.

Gupta A, Mishra N, Tripathi A, Vardhan M, Kushwaha DS. An Improved History-Based Test Prioritization Technique Technique Using Code Coverage. Adv Comput Commun Eng Technol. 2015 Nov; 315: 437-448.

Anderson J, Salem S, Do H. Improving the effectiveness of test suite through mining historical data. In MSR 2014: Proc 11th Work Conf Min Softw Repos; 2014. p. 142-151.

Noor TB, Hemmati H. A similarity- based approach for test case prioritization using historical failure data. In 2015 IEEE 26th ISSRE; 2015; Gaithersbury, MD. p. 58-68.

Goyal S, Mishra P, Lamichhane A, Gandhi P. Software Test Case Optimization Using Genetic Algorithm. Int J Sci Eng Sci. 2018; 1(12): 69-73.

Bhawna, Kumar G, Bhatia PK. Software Test Case Reduction using Genetic Algorithm: A Modified Approach. Int J Innov Sci Eng Technol. 2016 May; 3(5): 349-354.

Priyanka, Kumar R, Nipur. Generation of optimized and effective test case : A proposed model. Int J Eng Sci Math. 2017 Jul; 6(3): 115-123.

Mateen A, Nazir M, Awan SA. Optimization of Test Case Generation using Genetic Algorithm (GA). Int J Comput Appl. 2016 Oct; 151(7): 6-14.

Akour M, Abuwardih L, Alhindawi N, Alshboul A. Test Case Minimization using Genetic Algorithm: Pilot Study. In 2018 8th Int Conf CSIT; 2018. p. 66-70.

Serdyukov KE, Avdeenko TV. Using genetic algorithm for generating optimal data sets to automatic testing the program code. Inf Technol Nanotechnol. 2019 Jan;: 173-182.