# Test Suite Prioritization Based on Optimization Approach Using Reinforcement Learning
## 2 Related Work
They use a heuristic function to authorize the memory information related to paths for fault coverage. The ants' movement is recorded, and paths are updated until or unless no ant moves on any path. Then they prioritize the test suite based on these paths. The test cases covering all faults in minimum time are given high priority. mills introduced fault seeding in 1972 [27], in which the effectiveness of the test suite is presented using a comparison of the total seeded faults, and total detected faults. There are different software testing techniques for injecting faults [28; 29; 30; 24]. Table 1 presents a comparative analysis of the proposed approach with existing TSP techniques. The summary is based on the type of methodology, test suite minimization (TSM), TSP, test suite selection criterion, the contribution of the new software platform, the use of AI-based models, and relative demerits. The test suite selection criterion includes the analysis approach based on fault detection effectiveness (FDE) and CC. \begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline
**S.No Methodology** & **TSM** & **TSP** & **Test Suite Selection Criterion** & \
\begin{tabular}{c} **Software Platform** \\ **Developed** \\ \end{tabular} &
\begin{tabular}{c} **AI-Based** \\ **Model** \\ \end{tabular} & **Optimization** & **Relative Demerits** \\ \hline
1 & Modification based & yes & Yes &
\begin{tabular}{c} Correlation index based on FDE \\ and CC \\ \end{tabular} & No & No & No & Both techniques can give good results \\
2 & affected by the code lines & No & Yes & Yes & No & No & Proposed TSP approach can identify errors and \\  & modification [33] & No & Yes & & Yes & No &
\begin{tabular}{c} Total suite is prioritize test suites. \\ \end{tabular} \\
3 & Analysis based on & No & Yes & Fault detection percentage & yes & Yes & No & Optimization based TSP performs better. \\  & 9 mechanisms [34] & No & Yes & \begin{tabular}{c} Analyzing dataflows and \\ tracking executions \\ \end{tabular} & Yes & No & No & Regression tests are reduced, but no test suites are prioritized. \\  & 9 mechanisms [35] & No & Yes & CC & No & Yes & Yes & GA perform well, while greedy approaches are surprisingly effective \\  & 5 algorithms [36] & No & Yes & \begin{tabular}{c} Finding traces from user session \\ data and selection using GA \\ \end{tabular} & No & yes & Yes & \begin{tabular}{c} Decrease execution time but no increase in fault \\ detection rate. \\ \end{tabular} \\  & Methodology based on & No & Yes & \
\begin{tabular}{c} Finding traces from user session \\ data and selection using GA \\ \end{tabular} & No & yes & Yes &
\begin{tabular}{c} Proof of concepts on 3 projects, performance \\ analysis shows an increase in the granularity of \\ fitness function, leading to TSM. \\ \end{tabular} \\
8 & Mean percentage of fault & No & Yes & CC and Runtime & Yes & yes & Yes & Yes \\  & detection rate [39] & No & Yes & CC and Runtime & Yes & yes & Yes \\
9 & Partial CC [39] & & & path building and optimal path & & yes & Yes & Solve the triangle problem and reduces test suite. \\  &
\begin{tabular}{c} Multi-objective \\ optimization [40] \\ \end{tabular} & No & Yes & Requirement coverage and cost & No & yes & Yes & Minimize the cost and maximize the coverage \\
11 & Fuzzy system[41] & No & Yes & selection based on defects and test coverage & Yes & Yes & No & Case studies based on the GSM test database and the proposed defects detection rate in a short time. The proposed expert system is best suited for multi-objective optimization based on test suite reduction. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of proposed approach with existing TSP techniques.