Skip to main content
Erschienen in: Wireless Personal Communications 3/2022

28.10.2021

Design of a Novel Weighted-Multicriteria Analysis Model for Effective Test Case Prioritization for Network and Robotic Projects

verfasst von: Kapil Juneja

Erschienen in: Wireless Personal Communications | Ausgabe 3/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Network and real-time projects requires special and effective testing consideration before implementing in real environment. The effective test-sequence not only reduces the actual testing time but also reduces the cost and efforts. The design-flow diagram and the module attributes play an essential role in generating a valid path sequence. In this paper, an automated and generalized framework is designed that processes the code project and generates the optimized test sequence. In the earlier stage of this framework, the structural and relational features of program code are extracted, and the design flow-diagram is constructed. While constructing the diagram, the design-time features are computed, connected, and updated with each node. The connectivity, dependency, positional, and contributional features are computed for each node. In the second stage, this weighted design-flow diagram and fault weights are used in a combined form for deciding the low-cost test sequence. The proposed framework is applied to five network, security and robotics based code sources. The comparative analysis is done against the Random Search, Genetics, and REMAP methods for test sequence generation. The proposed model achieved an average APFDc score of 87.11%. The proposed model achieved 3.3% gain over REMAP (Ripper + IBEA(3Obj)), 7.9% gain over REMAP (Ripper + SPEA2(2Obj)), 20.63% gain over Genetics (3Objects), 21.05% gain over Genetics (2Objects), 34.5% gain over Random Forest (3 Objects) and 34.96% gain over Random forests(3Objs). The results confirm that the proposed model achieved the higher APFDc score than state-of-art methods.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Ramler, R., Buchgeher, G., & Klammer, C. (2018). Adapting automated test generation to GUI testing of industry applications. Information and Software Technology, 93, 248–263.CrossRef Ramler, R., Buchgeher, G., & Klammer, C. (2018). Adapting automated test generation to GUI testing of industry applications. Information and Software Technology, 93, 248–263.CrossRef
2.
Zurück zum Zitat Garousi, V., Felderer, M., & Kılıçaslan, F. N. (2019). A survey on software testability. Information and Software Technology, 108, 35–64.CrossRef Garousi, V., Felderer, M., & Kılıçaslan, F. N. (2019). A survey on software testability. Information and Software Technology, 108, 35–64.CrossRef
3.
Zurück zum Zitat Ahmad, T., Iqbal, J., Ashraf, A., Truscan, D., & Porres, I. (2019). Model-based testing using UML activity diagrams: A systematic mapping study. Computer Science Review, 33, 98–112.CrossRef Ahmad, T., Iqbal, J., Ashraf, A., Truscan, D., & Porres, I. (2019). Model-based testing using UML activity diagrams: A systematic mapping study. Computer Science Review, 33, 98–112.CrossRef
4.
Zurück zum Zitat Khurana, N., & Chillar, R. S. (2015). Test Case Generation and Optimization using UML Models and Genetic Algorithm. Procedia Computer Science, 57, 996–1004.CrossRef Khurana, N., & Chillar, R. S. (2015). Test Case Generation and Optimization using UML Models and Genetic Algorithm. Procedia Computer Science, 57, 996–1004.CrossRef
5.
Zurück zum Zitat Campos, S., Grumberg, O., Yorav, K., & Fady, C. (2004). Test sequence generation and model checking using dynamic transition relations. International Journal on Software Tools for Technology Transfer, 6(2), 174–182.CrossRef Campos, S., Grumberg, O., Yorav, K., & Fady, C. (2004). Test sequence generation and model checking using dynamic transition relations. International Journal on Software Tools for Technology Transfer, 6(2), 174–182.CrossRef
6.
Zurück zum Zitat Mukherjee, R., & Patnaik, K. S. (2018). A survey on different approaches for software test case prioritization. Journal of King Saud University - Computer and Information Sciences, 33, 1041–1054.CrossRef Mukherjee, R., & Patnaik, K. S. (2018). A survey on different approaches for software test case prioritization. Journal of King Saud University - Computer and Information Sciences, 33, 1041–1054.CrossRef
7.
Zurück zum Zitat Hemmati, H. (2019). Chapter four - advances in techniques for test prioritization. Advances in Computers, 112, 185–221.CrossRef Hemmati, H. (2019). Chapter four - advances in techniques for test prioritization. Advances in Computers, 112, 185–221.CrossRef
8.
Zurück zum Zitat Bukhsh, F. A., Bukhsh, Z. A., & Daneva, M. (2020). A systematic literature review on requirement prioritization techniques and their empirical evaluation. Computer Standards & Interfaces, 69, 103389.CrossRef Bukhsh, F. A., Bukhsh, Z. A., & Daneva, M. (2020). A systematic literature review on requirement prioritization techniques and their empirical evaluation. Computer Standards & Interfaces, 69, 103389.CrossRef
9.
Zurück zum Zitat Jiang, B., Zhang, Z., Chan, W. K., Tse, T. H., & Chen, T. Y. (2012). How well does test case prioritization integrate with statistical fault localization? Information and Software Technology, 54(7), 739–758.CrossRef Jiang, B., Zhang, Z., Chan, W. K., Tse, T. H., & Chen, T. Y. (2012). How well does test case prioritization integrate with statistical fault localization? Information and Software Technology, 54(7), 739–758.CrossRef
10.
Zurück zum Zitat Farooq, F., & Nadeem, A. (2017). A fault based approach to test case prioritization. In International conference on frontiers of information technology (FIT) (pp. 52–57). Farooq, F., & Nadeem, A. (2017). A fault based approach to test case prioritization. In International conference on frontiers of information technology (FIT) (pp. 52–57).
11.
Zurück zum Zitat Wang, Y., Zhao, X., & Ding, X. (2015). An effective test case prioritization method based on fault severity. In: 6th IEEE international conference on software engineering and service science (ICSESS) (pp. 737–741). Wang, Y., Zhao, X., & Ding, X. (2015). An effective test case prioritization method based on fault severity. In: 6th IEEE international conference on software engineering and service science (ICSESS) (pp. 737–741).
12.
Zurück zum Zitat Nayak, S., Kumar, C., Tripathi, S., & Majumdar, N. (2019). An improved approach to enhance the test case prioritization efficiency. In Proceedings of ICETIT (pp. 1119–1128). Nayak, S., Kumar, C., Tripathi, S., & Majumdar, N. (2019). An improved approach to enhance the test case prioritization efficiency. In Proceedings of ICETIT (pp. 1119–1128).
13.
Zurück zum Zitat Indumathi, C. P., & Selvamani, K. (2015). Test cases prioritization using open dependency structure algorithm. Procedia Computer Science, 48, 250–255.CrossRef Indumathi, C. P., & Selvamani, K. (2015). Test cases prioritization using open dependency structure algorithm. Procedia Computer Science, 48, 250–255.CrossRef
14.
Zurück zum Zitat Srikanth, H., Hettiarachchi, C., & Do, H. (2016). Requirements based test prioritization using risk factors: An industrial study. Information and Software Technology, 69, 71–83.CrossRef Srikanth, H., Hettiarachchi, C., & Do, H. (2016). Requirements based test prioritization using risk factors: An industrial study. Information and Software Technology, 69, 71–83.CrossRef
15.
Zurück zum Zitat Kavitha, R., & Kavitha, V. R., & Suresh Kumar, N. (2010). Requirement based test case prioritization. In International conference on communication control and computing technologies (pp. 826–829). Kavitha, R., & Kavitha, V. R., & Suresh Kumar, N. (2010). Requirement based test case prioritization. In International conference on communication control and computing technologies (pp. 826–829).
16.
Zurück zum Zitat Dobuneh, M. R. N., Jawawi, D. N. A., Ghazali, M., & Malakooti, M. V. (2014). Development test case prioritization technique in regression testing based on hybrid criteria. In 8th Malaysian software engineering conference (MySEC) (pp. 301–305). Dobuneh, M. R. N., Jawawi, D. N. A., Ghazali, M., & Malakooti, M. V. (2014). Development test case prioritization technique in regression testing based on hybrid criteria. In 8th Malaysian software engineering conference (MySEC) (pp. 301–305).
17.
Zurück zum Zitat Mohapatra, S. K., & Prasad, S. (2013). Evolutionary search algorithms for test case prioritization. In International conference on machine intelligence and research advancement (pp. 115–119). Mohapatra, S. K., & Prasad, S. (2013). Evolutionary search algorithms for test case prioritization. In International conference on machine intelligence and research advancement (pp. 115–119).
18.
Zurück zum Zitat Chen, J., et al. (2018). Test case prioritization for object-oriented software: An adaptive random sequence approach based on clustering. Journal of Systems and Software, 135, 107–125.CrossRef Chen, J., et al. (2018). Test case prioritization for object-oriented software: An adaptive random sequence approach based on clustering. Journal of Systems and Software, 135, 107–125.CrossRef
19.
Zurück zum Zitat Kaliraj, S., & Bharathi, A. (2019). Path testing based reliability analysis framework of component based software system. Measurement, 144, 20–32.CrossRef Kaliraj, S., & Bharathi, A. (2019). Path testing based reliability analysis framework of component based software system. Measurement, 144, 20–32.CrossRef
20.
Zurück zum Zitat Mohi-Aldeen, S. M., Mohamad, R., & Deris, S. (2016). Application of Negative Selection Algorithm (NSA) for test data generation of path testing. Applied Soft Computing, 49, 1118–1128.CrossRef Mohi-Aldeen, S. M., Mohamad, R., & Deris, S. (2016). Application of Negative Selection Algorithm (NSA) for test data generation of path testing. Applied Soft Computing, 49, 1118–1128.CrossRef
21.
Zurück zum Zitat Huang, R., Zhang, Q., Towey, D., Sun, W., & Chen, J. (2020). Regression test case prioritization by code combinations coverage. Journal of Systems and Software, 169, 110712.CrossRef Huang, R., Zhang, Q., Towey, D., Sun, W., & Chen, J. (2020). Regression test case prioritization by code combinations coverage. Journal of Systems and Software, 169, 110712.CrossRef
22.
Zurück zum Zitat Li, J., Zhang, Y., & Bian, J. (2019). Defect-Oriented Test Case Prioritization. Chinese Intelligent Systems Conference, Lecture Notes in Electrical Engineering, 593, 651–659.CrossRef Li, J., Zhang, Y., & Bian, J. (2019). Defect-Oriented Test Case Prioritization. Chinese Intelligent Systems Conference, Lecture Notes in Electrical Engineering, 593, 651–659.CrossRef
23.
Zurück zum Zitat Huang, R., et al. (2020). Abstract test case prioritization using repeated small-strength level-combination coverage. IEEE Transactions on Reliability, 69(1), 349–372.CrossRef Huang, R., et al. (2020). Abstract test case prioritization using repeated small-strength level-combination coverage. IEEE Transactions on Reliability, 69(1), 349–372.CrossRef
24.
Zurück zum Zitat Wang, R., Li, Z., Jiang, S., & Tao, C. (2020). Regression test case prioritization based on fixed size of candidate set ART algorithm. International Journal of Software Engineering and Knowledge Engineering, 30(3), 291–320.CrossRef Wang, R., Li, Z., Jiang, S., & Tao, C. (2020). Regression test case prioritization based on fixed size of candidate set ART algorithm. International Journal of Software Engineering and Knowledge Engineering, 30(3), 291–320.CrossRef
25.
Zurück zum Zitat Pradhan, D., Wang, S., Ali, S., Yue, T., & Liaaen, M. (2018). REMAP: Using rule mining and multi-objective search for dynamic test case prioritization. In IEEE 11th international conference on software testing, verification and validation (ICST) (pp. 46–57). Pradhan, D., Wang, S., Ali, S., Yue, T., & Liaaen, M. (2018). REMAP: Using rule mining and multi-objective search for dynamic test case prioritization. In IEEE 11th international conference on software testing, verification and validation (ICST) (pp. 46–57).
26.
Zurück zum Zitat Mahdieh, M., Mirian-Hosseinabadi, S.-H., Etemadi, K., Nosrati, A., & Jalali, S. (2020). Incorporating fault-proneness estimations into coverage-based test case prioritization methods. Information and Software Technology, 121, 106269.CrossRef Mahdieh, M., Mirian-Hosseinabadi, S.-H., Etemadi, K., Nosrati, A., & Jalali, S. (2020). Incorporating fault-proneness estimations into coverage-based test case prioritization methods. Information and Software Technology, 121, 106269.CrossRef
27.
Zurück zum Zitat Wei, D., Sun, Q., Wang, X., Zhang, T., & Chen, B. (2020). A model-based test case prioritization approach based on fault urgency and severity. International Journal of Software Engineering and Knowledge Engineering, 30(2), 263–290.CrossRef Wei, D., Sun, Q., Wang, X., Zhang, T., & Chen, B. (2020). A model-based test case prioritization approach based on fault urgency and severity. International Journal of Software Engineering and Knowledge Engineering, 30(2), 263–290.CrossRef
28.
Zurück zum Zitat Eldrandaly, K., Ellatif, M. A., & Zaki, N. (2020). A proposed framework for test suite prioritization and reduction using the clustering data mining technique. Journal of Theoretical and Applied Information Technology, 98(2), 290–307. Eldrandaly, K., Ellatif, M. A., & Zaki, N. (2020). A proposed framework for test suite prioritization and reduction using the clustering data mining technique. Journal of Theoretical and Applied Information Technology, 98(2), 290–307.
29.
Zurück zum Zitat Mahali, P., & Mohapatra, D. P. (2018). Model based test case prioritization using UML behavioural diagrams and association rule mining. International Journal of System Assurance Engineering and Management, 9(5), 1063–1079. Mahali, P., & Mohapatra, D. P. (2018). Model based test case prioritization using UML behavioural diagrams and association rule mining. International Journal of System Assurance Engineering and Management, 9(5), 1063–1079.
30.
Zurück zum Zitat Wang, Y., Zhu, Z., Yang, Bo., Guo, F., & Hai, Yu. (2018). Using reliability risk analysis to prioritize test cases. Journal of Systems and Software, 139, 14–31.CrossRef Wang, Y., Zhu, Z., Yang, Bo., Guo, F., & Hai, Yu. (2018). Using reliability risk analysis to prioritize test cases. Journal of Systems and Software, 139, 14–31.CrossRef
31.
Zurück zum Zitat Khalilian, A., Azgomi, M. A., & Fazlalizadeh, Y. (2012). An improved method for test case prioritization by incorporating historical test case data. Science of Computer Programming, 78(1), 93–116.MATHCrossRef Khalilian, A., Azgomi, M. A., & Fazlalizadeh, Y. (2012). An improved method for test case prioritization by incorporating historical test case data. Science of Computer Programming, 78(1), 93–116.MATHCrossRef
32.
Zurück zum Zitat Hettiarachchi, C., Do, H., & Choi, B. (2016). Risk-based test case prioritization using a fuzzy expert system. Information and Software Technology, 69, 1–15.CrossRef Hettiarachchi, C., Do, H., & Choi, B. (2016). Risk-based test case prioritization using a fuzzy expert system. Information and Software Technology, 69, 1–15.CrossRef
33.
Zurück zum Zitat Huang, C.-Y., Chang, J.-R., & Chang, Y.-H. (2010). Design and analysis of GUI test-case prioritization using weight-based methods. Journal of Systems and Software, 83(4), 646–659.CrossRef Huang, C.-Y., Chang, J.-R., & Chang, Y.-H. (2010). Design and analysis of GUI test-case prioritization using weight-based methods. Journal of Systems and Software, 83(4), 646–659.CrossRef
34.
Zurück zum Zitat Yadav, D. K., & Dutta, S. (2016). Test case prioritization technique based on early fault detection using fuzzy logic. In 3rd international conference on computing for sustainable global development (INDIACom) (pp. 1033–1036). Yadav, D. K., & Dutta, S. (2016). Test case prioritization technique based on early fault detection using fuzzy logic. In 3rd international conference on computing for sustainable global development (INDIACom) (pp. 1033–1036).
35.
Zurück zum Zitat Jahan, H., Feng, Z., & Mahmud, S. M. H. (2020). Risk-based test case prioritization by correlating system methods and their associated risks. Arabian Journal for Science and Engineering, 45, 6125–6138.CrossRef Jahan, H., Feng, Z., & Mahmud, S. M. H. (2020). Risk-based test case prioritization by correlating system methods and their associated risks. Arabian Journal for Science and Engineering, 45, 6125–6138.CrossRef
36.
Zurück zum Zitat Ferrer, J., Kruse, P. M., Chicano, F., & Alba, E. (2015). Search based algorithms for test sequence generation in functional testing. Information and Software Technology, 58, 419–432.CrossRef Ferrer, J., Kruse, P. M., Chicano, F., & Alba, E. (2015). Search based algorithms for test sequence generation in functional testing. Information and Software Technology, 58, 419–432.CrossRef
37.
Zurück zum Zitat Lam, S. S. B., Hari Prasad Raju, M. L., Kiran, U. M., Ch, S., & Srivastav, P. R. (2012). Automated generation of independent paths and test suite optimization using artificial bee colony. Procedia Engineering, 30, 191–200.CrossRef Lam, S. S. B., Hari Prasad Raju, M. L., Kiran, U. M., Ch, S., & Srivastav, P. R. (2012). Automated generation of independent paths and test suite optimization using artificial bee colony. Procedia Engineering, 30, 191–200.CrossRef
38.
Zurück zum Zitat Banias, O. (2019). Test case selection-prioritization approach based on memoization dynamic programming algorithm. Information and Software Technology, 115, 119–130.CrossRef Banias, O. (2019). Test case selection-prioritization approach based on memoization dynamic programming algorithm. Information and Software Technology, 115, 119–130.CrossRef
39.
Zurück zum Zitat Di Nucci, D., Panichella, A., Zaidman, A., & De Lucia, A. (2020). A test case prioritization genetic algorithm guided by the hypervolume indicator. IEEE Transactions on Software Engineering, 46(6), 674–696.CrossRef Di Nucci, D., Panichella, A., Zaidman, A., & De Lucia, A. (2020). A test case prioritization genetic algorithm guided by the hypervolume indicator. IEEE Transactions on Software Engineering, 46(6), 674–696.CrossRef
40.
Zurück zum Zitat Hemmati, H., Arcuri, A., & Briand, L. (2013). Achieving scalable model-based testing through test case diversity. ACM Transactions on Software Engineering and Methodology, 22(1), 63–78.CrossRef Hemmati, H., Arcuri, A., & Briand, L. (2013). Achieving scalable model-based testing through test case diversity. ACM Transactions on Software Engineering and Methodology, 22(1), 63–78.CrossRef
41.
Zurück zum Zitat Hemmati, H., & Briand, L. (2010). An industrial investigation of similarity measures for model-based test case selection. In Proceedings of 21st international conference on software reliability engineering (pp. 141–150). Hemmati, H., & Briand, L. (2010). An industrial investigation of similarity measures for model-based test case selection. In Proceedings of 21st international conference on software reliability engineering (pp. 141–150).
42.
Zurück zum Zitat Jiang, B., & Chan, W. K. (2015). Input-based adaptive randomized test case prioritization: A local beam search approach. Journal of Systems and Software, 105, 91–106.CrossRef Jiang, B., & Chan, W. K. (2015). Input-based adaptive randomized test case prioritization: A local beam search approach. Journal of Systems and Software, 105, 91–106.CrossRef
43.
Zurück zum Zitat Sayyari, F., & Emadi, S. (2015). Automated generation of software testing path based on ant colony. In International congress on technology, communication and knowledge (ICTCK) (pp. 435–440). Sayyari, F., & Emadi, S. (2015). Automated generation of software testing path based on ant colony. In International congress on technology, communication and knowledge (ICTCK) (pp. 435–440).
44.
Zurück zum Zitat Preeti & Chaudhary, J. (2014). An improved genetic approach for test path generation. In International conference on advances in engineering & technology research (pp. 1–5). Preeti & Chaudhary, J. (2014). An improved genetic approach for test path generation. In International conference on advances in engineering & technology research (pp. 1–5).
45.
Zurück zum Zitat Singh, M., Srivastava, V. M., Gaurav, K., & Gupta, P. K. (2017). Automatic test data generation based on multi-objective ant lion optimization algorithm. In: Pattern recognition association of South Africa and robotics and mechatronics (PRASA-RobMech) (pp. 168–174). Singh, M., Srivastava, V. M., Gaurav, K., & Gupta, P. K. (2017). Automatic test data generation based on multi-objective ant lion optimization algorithm. In: Pattern recognition association of South Africa and robotics and mechatronics (PRASA-RobMech) (pp. 168–174).
46.
Zurück zum Zitat Panthi, V., & Mohapatra, D. P. (2018). Firely optimization technique based test scenario generation and prioritization. Journal of Applied Research and Technology, 16, 466–483.CrossRef Panthi, V., & Mohapatra, D. P. (2018). Firely optimization technique based test scenario generation and prioritization. Journal of Applied Research and Technology, 16, 466–483.CrossRef
47.
Zurück zum Zitat Panthi, V., & Mohapatra, D. P. (2017). A Framework for generating prioritized test scenarios using firefly optimization. International Journal of Computing Science & Mathematics, 8(3), 228–237.MATHCrossRef Panthi, V., & Mohapatra, D. P. (2017). A Framework for generating prioritized test scenarios using firefly optimization. International Journal of Computing Science & Mathematics, 8(3), 228–237.MATHCrossRef
48.
Zurück zum Zitat Dhareula, P., & Ganpati, A. (2020). Flower pollination algorithm for test case prioritization in regression testing. ICT Analysis and Applications, Lecture Notes in Networks and Systems, 93, 155–167.CrossRef Dhareula, P., & Ganpati, A. (2020). Flower pollination algorithm for test case prioritization in regression testing. ICT Analysis and Applications, Lecture Notes in Networks and Systems, 93, 155–167.CrossRef
49.
Zurück zum Zitat Cvetković, J., & Cvetković, M. (2019). Evaluation of UML diagrams for test cases generation: Case study on depression of internet addiction. Physica A: Statistical Mechanics and its Applications, 525, 1351–1359.CrossRef Cvetković, J., & Cvetković, M. (2019). Evaluation of UML diagrams for test cases generation: Case study on depression of internet addiction. Physica A: Statistical Mechanics and its Applications, 525, 1351–1359.CrossRef
50.
Zurück zum Zitat Vivekanandan, K., Megala, T., & Chandini, P. (2016). Automatic generation of basis test path using clonal selection algorithm. In International conference on information communication and embedded systems (ICICES) (pp. 1–4). Vivekanandan, K., Megala, T., & Chandini, P. (2016). Automatic generation of basis test path using clonal selection algorithm. In International conference on information communication and embedded systems (ICICES) (pp. 1–4).
51.
Zurück zum Zitat Waheed, S. Z., & Qamar, U. (2015). Data flow based test case generation algorithm for object oriented integration testing. In 6th IEEE international conference on software engineering and service science (ICSESS) (pp. 423–427). Waheed, S. Z., & Qamar, U. (2015). Data flow based test case generation algorithm for object oriented integration testing. In 6th IEEE international conference on software engineering and service science (ICSESS) (pp. 423–427).
52.
Zurück zum Zitat Wijayasiriwardhane, T. K., Wijayarathna, P. G., & Karunarathna, D. D. (2011). An automated tool to generate test cases for performing basis path testing. In International conference on advances in ICT for emerging regions (ICTer) (pp. 95–101). Wijayasiriwardhane, T. K., Wijayarathna, P. G., & Karunarathna, D. D. (2011). An automated tool to generate test cases for performing basis path testing. In International conference on advances in ICT for emerging regions (ICTer) (pp. 95–101).
53.
Zurück zum Zitat Qingfeng, D., & Xiao, D. (2011). An improved algorithm for basis path testing. In International conference on business management and electronic information (pp. 175–178). Qingfeng, D., & Xiao, D. (2011). An improved algorithm for basis path testing. In International conference on business management and electronic information (pp. 175–178).
54.
Zurück zum Zitat Chi, J., et al. (2020). Relation-based test case prioritization for regression testing. Journal of Systems and Software, 163, 110539.CrossRef Chi, J., et al. (2020). Relation-based test case prioritization for regression testing. Journal of Systems and Software, 163, 110539.CrossRef
55.
Zurück zum Zitat Yadav, D. K., & Dutta, S. (2020). Regression test case selection and prioritization for object oriented software. Microsystem Technologies, 26, 1463–1477.CrossRef Yadav, D. K., & Dutta, S. (2020). Regression test case selection and prioritization for object oriented software. Microsystem Technologies, 26, 1463–1477.CrossRef
56.
Zurück zum Zitat Babu, J. S., et al. (2020). Test case prioritization for regression testing based on Cc metric analyzer. International Journal of Scientific and Technology Research, 9(2), 3345–3348. Babu, J. S., et al. (2020). Test case prioritization for regression testing based on Cc metric analyzer. International Journal of Scientific and Technology Research, 9(2), 3345–3348.
57.
Zurück zum Zitat Zhou, Z. Q., Liu, C., Chen, T. Y., Tse, T. H., & Susilo, W. (2020). Beating random test case prioritization. IEEE Transactions on Reliability, 2020, 1–22. Zhou, Z. Q., Liu, C., Chen, T. Y., Tse, T. H., & Susilo, W. (2020). Beating random test case prioritization. IEEE Transactions on Reliability, 2020, 1–22.
58.
Zurück zum Zitat Pradhan, D., Wang, S., Ali, S., Yue, T., & Liaaen, M. (2019). Employing rule mining and multi-objective search for dynamic test case prioritization. Journal of Systems and Software, 153, 86–104.CrossRef Pradhan, D., Wang, S., Ali, S., Yue, T., & Liaaen, M. (2019). Employing rule mining and multi-objective search for dynamic test case prioritization. Journal of Systems and Software, 153, 86–104.CrossRef
Metadaten
Titel
Design of a Novel Weighted-Multicriteria Analysis Model for Effective Test Case Prioritization for Network and Robotic Projects
verfasst von
Kapil Juneja
Publikationsdatum
28.10.2021
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2022
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-021-09251-z

Weitere Artikel der Ausgabe 3/2022

Wireless Personal Communications 3/2022 Zur Ausgabe

Neuer Inhalt