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Table 4. Results obtained from TSP_ACO Algorithm sequence number 5 has the highest priority or rank 1, because it contains the maximum number of decision nodes. Its total strength computed and found to be 56.10. It contains a total of 16 nodes, in which 4 nodes are decision nodes. On the other hand, test sequence number 3, test sequence number 2 and test sequence number | all got same number of decision nodes. Test sequence! got the least strength value and assigned lowest rank that is 6. Hence, ACO is proven to be an effective approach to optimize test sequence generation.

Table 4 Results obtained from TSP_ACO Algorithm sequence number 5 has the highest priority or rank 1, because it contains the maximum number of decision nodes. Its total strength computed and found to be 56.10. It contains a total of 16 nodes, in which 4 nodes are decision nodes. On the other hand, test sequence number 3, test sequence number 2 and test sequence number | all got same number of decision nodes. Test sequence! got the least strength value and assigned lowest rank that is 6. Hence, ACO is proven to be an effective approach to optimize test sequence generation.

Related Figures (10)

Figure 2. Architecture of Proposed Model
Figure 2. Architecture of Proposed Model
Figure 4. Generated graph through Graphviz 5.2 Materials & Methods
Figure 4. Generated graph through Graphviz 5.2 Materials & Methods
Table 2. Generated Node (Nd) Information
Table 2. Generated Node (Nd) Information
Table 5. Fault Description Table 6. Fault Matrix before Prioritization
Table 5. Fault Description Table 6. Fault Matrix before Prioritization
Table 7. Comparison of Case Studies 98 nodes that are used is used to optimize th and the obtained resu executed on the same management system) ts validates our proposed helps different banks and has many features such as eK YC Processing, Functionality, NPA M to generate a total of 72 number of test paths. Then, the proposed optimization algorithm e number of test path, which results to 46 number of test paths. This evaluation process approach using ACO. Similarly, the rest of the case studies are discussed context and the obtained results are shown in Table 7. PLMS (Personal loan financial institution to manage their lending issues. This system Borrower Profile management, Co-Borrower Loan Reschedule anagement. Collateral Management etc. For such features, on the context of a particular scenario, the constructed Activity diagram. This diagram has 34 number of nodes that are executed and we found 12 number of optimal test paths from total of 24 number of test paths. Like this, we have experimented on 6 different case study based on the real life software products and the experimental evaluation has achieved an average of 52.16% reduction of test paths. Table 7 shows a comparison of different case study based on total number of test paths and optimized test paths obtained using our proposed approach.
Table 7. Comparison of Case Studies 98 nodes that are used is used to optimize th and the obtained resu executed on the same management system) ts validates our proposed helps different banks and has many features such as eK YC Processing, Functionality, NPA M to generate a total of 72 number of test paths. Then, the proposed optimization algorithm e number of test path, which results to 46 number of test paths. This evaluation process approach using ACO. Similarly, the rest of the case studies are discussed context and the obtained results are shown in Table 7. PLMS (Personal loan financial institution to manage their lending issues. This system Borrower Profile management, Co-Borrower Loan Reschedule anagement. Collateral Management etc. For such features, on the context of a particular scenario, the constructed Activity diagram. This diagram has 34 number of nodes that are executed and we found 12 number of optimal test paths from total of 24 number of test paths. Like this, we have experimented on 6 different case study based on the real life software products and the experimental evaluation has achieved an average of 52.16% reduction of test paths. Table 7 shows a comparison of different case study based on total number of test paths and optimized test paths obtained using our proposed approach.
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