Information Technology has rapidly developed in recent years and software systems can play a critical role in the symmetry of the technology. Regarding the field of software testing, white-box unit-level testing constitutes the backbone of all other testing techniques, as testing can be entirely implemented by considering the source code of each System Under Test (SUT). In unit-level white-box testing, mutants can be used; these mutants are artificially generated faults seeded in each SUT that behave similarly to the realistic ones. Executing test cases against mutants results in the adequacy (mutation) score of each test case. Efficient Genetic Algorithm (GA)-based methods have been proposed to address different problems in white-box unit testing and, in particular, issues of mutation testing techniques. In this research paper, a new approach, which integrates the path coverage-based testing method with the novel idea of tracing a Fault Detection Matrix (FDM) to achieve maximum mutation coverage, is proposed. The proposed real coded GA for mutation testing is designed to achieve the highest Mutation Score, and it is thus named RGA-MS. The approach is implemented in two phases: path coverage-based test data are initially generated and stored in an optimized test suite. In the next phase, the test suite is executed to kill the mutants present in the SUT. The proposed method aims to achieve the minimum test dataset, having at the same time the highest Mutation Score by removing duplicate test data covering the same mutants. The proposed approach is implemented on the same SUTs as these have been used for path testing. We proved that the RGA-MS approach can cover maximum mutants with a minimum number of test cases. Furthermore, the proposed method can generate a maximum path coverage-based test suite with minimum test data generation compared to other algorithms. In addition, all mutants in the SUT can be covered by less number of test data with no duplicates. Ultimately, the generated optimal test suite is trained to achieve the highest Mutation Score. GA is used to find the maximum mutation coverage as well as to delete the redundant test cases.

The stock price index prediction is a very challenging task that's because the market has a very complicated nonlinear movement system. This fluctuation is influenced by many different factors. Multiple examples demonstrate the suitability of Machine Learning (ML) models like Neural Network algorithms (NN) and Long Short-Term Memory (LSTM) for such time series predictions, as well as how frequently they produce satisfactory outcomes. However, relatively few studies have employed robust feature engineering sequence models to forecast future prices. In this paper, we propose a cutting-edge stock price prediction model based on a Deep Learning (DL) technique. We chose the stock data for Intel, the firm with one of the quickest growths in the past ten years. The experimental results demonstrate that, for predicting this particular stock time series, our suggested model outperforms the current Gated Recurrent Unit (GRU) model. Our prediction approach reduces inaccuracy by taking into account the random nature of data on a big scale.

Regression testing is an activity performed in maintenance level of software. Growing of software test suite results in high cost and which also damages the software system. Test case prioritization is used to prioritize the most appropriate test cases. This research paper presents a test case prioritization and minimization technique by taking various factors involved such as risk exposure, requirement factor, statement coverage data and execution time. There are different factors of test cases such as statement coverage data, requirements factors, risk exposure and execution time. Genetic algorithm is used to prioritize test cases and further, test cases are minimized based on some given time constraint. The effectiveness of the prioritized test cases is measured by APSC metric.

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