Abstract
The test case prioritization is the technique of Regression testing in which test cases are prioritized according to the changes which are done in the project. This work is based on manual slicing and automated slicing for test case prioritization to detect maximum number of faults from the project in which some changes are done for the new version release. The slicing is the technique which will divide the whole project function wise and detect associated functions. To increase the fault detection rate the automated technique is being applied in which multi-objective algorithm is been applied which calculates the function importance in the automated manner. In the simulation it is being analyzed that fault detection rate is increased and execution time is reduced with the implementation of automated test case prioritization as compared to manual test case prioritization in regression testing.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Catal, C.: Software fault prediction: a literature review and current trends. Expert Syst. Appl. 38, 4626–4636 (2011)
Malhotra, R.: A systematic review of machine learning techniques for software fault prediction. Appl. Soft Comput. 27, 504–518 (2015)
Menzies, T., Caglayan, B., Kocaguneli, E., Krall, J., Peters, F., Turhan, B.: The promise repository of empirical software engineering data. West Virginia University, Department of Computer Science (2012)
McCabe, T.J.: A complexity measure. IEEE Trans. Softw. Eng. 2, 308–320 (1976)
Halstead, M.H.: Elements of Software Science (Operating and Programming Systems Series). Elsevier Science Inc., Amsterdam (1977)
Guo, L., Ma, Y., Cukic, B., Singh, H.S.H.: Robust prediction of faultproneness by random forests. In: 15th International Symposium on Software Reliability Engineering (2004)
Kaur, A., Malhotra, R.: Application of random forest for predicting fault prone classes. In: International Conference on Advanced Computer Theory and Engineering, Thailand, 20–22 December, 2008
Khoshgoftaar, T., Allen, E.: Model software quality with classification trees. Recent Adv. Reliab. Qual. Eng. (2001)
Bener, A., Turhan, B.: Analysis of Naive Bayes’ assumptions on software fault data: an empirical study. Data Knowl. Eng. 68, 278–290 (2009)
Meiliana, Karim, S., Warnars, H.L.H.S., Soewito, B.: Software Metrics for fault prediction using machine learning approaches. IEEE 2017
Elbaum, S., Malishevsky, A.G., Rothermel, G.: Test case prioritization: a family of empirical studies. IEEE Trans. Softw. Eng. 28(2), 159–182 (2002)
Yohannese, C.W., Li, T., Simfukwe, M., Khurshid, F.: Ensembles based combined learning for improved software fault prediction: a comparative study. In: 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (2017)
Owhadi-Kareshk, M., Sedaghat, Y., Akbarzadeh, T.M.R.: Pre-training of an artificial neural network for software fault prediction. In: 7th International Conference on Computer and Knowledge Engineering (ICCKE 2017), 26–27 October 2017
Kaur, K., Kaur, P.: Evaluation of sampling techniques in software fault prediction using metrics and code smells. IEEE (2017)
Altinger, H., Herboldy, S., Schneemann, F., Grabowskiy, J., Wotawa, F.: Performance tuning for automotive software fault prediction. IEEE (2017)
Sultan, Z., Bhatti, S.N., Abbas, R., Asim Ali Shah, S.: Analytical review on test cases prioritization techniques: an empirical study. Int. J. Adv. Comput. Sci. Appl. 8(2) (2017)
Vescan, A., serban, C., Chisalita Creu, C., Dioan, L.: Requirement dependencies–based formal approach for test case prioritization in regression testing. IEEE (2017)
Ozturk, M.M.: Adapting code maintainability to bat-inspired test case prioritization. IEEE (2017)
Ding, J., Zhang, X.Y.: Comparison analysis of two test case prioritization approaches with the core idea of adaptive. IEEE (2017)
Chen, L., Fang, B., Shang, Z.: software fault prediction based on one class SVM. IEEE (2016)
Rathore, S.S., Kuamr, S.: Comparative analysis of neural network and genetic programming for number of software faults prediction. In: National Conference on Recent Advances in Electronics & Computer Engineering, RAECE-2015, 13–15 February 2015
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Thakur, A., Sharma, G. (2019). Neural Network Based Test Case Prioritization in Software Engineering. In: Luhach, A., Singh, D., Hsiung, PA., Hawari, K., Lingras, P., Singh, P. (eds) Advanced Informatics for Computing Research. ICAICR 2018. Communications in Computer and Information Science, vol 956. Springer, Singapore. https://doi.org/10.1007/978-981-13-3143-5_28
Download citation
DOI: https://doi.org/10.1007/978-981-13-3143-5_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3142-8
Online ISBN: 978-981-13-3143-5
eBook Packages: Computer ScienceComputer Science (R0)