Software program testing is the procedure of exercising a software component with a selected set of test cases as a way to discover defects and assess quality. Using software testing automation, especially the generating of testing data increases the effectiveness and efficiency of software testing as a whole. Instead of creating testing data from scratch, Big Data (BD) offers an important source of testing data. Although it is a good source, there is a need to select a proper set of testing data for the sake of selecting an optimal sub-domain input values from the BD. To refine the efficiency of software testing, this paper proposes a hybrid Genetic Algorithm and Binary Search (BSGA) technique that is used for detecting the error-prone path in a program. The BSGA combines the Genetic Algorithm (GA) with the Binary Search (BS) algorithm that uses the BD as input values for the program path coverage, and thus enhances the software testing. The BSGA represents a robust nonlinear search technique and a better quality solution, which therefore results in a cost reduction in the software testing industry. The experiments show that the results approved the impact of using the BS to enhance the performance of the GA, in terms of finding optimal test cases and test data for the input Big Data domain values. Whereas, these results minimize the cost of testing.

In Software Engineering (SE), metrics are used for detecting software design problems (bad smells) like the large-class bad smell, where a lot of different metrics were defined to find out the existence of this problem in the design of a class. Examples of these metrics are size metrics, cohesion metrics, and coupling metrics. Selecting the right metrics to detect the large-class bad smell is a common problem, and it is usually accomplished manually. The questions remain: Can a module with the best combination of two metrics, for detecting the problem of large-class bad smell, be formed automatically rather than manually? And how is this double valued threshold determined to be used to infer the existence of this problem? This paper proposes the Hybrid Approach to detect Large Class Bad Smell (HA-LCBS). This approach utilizes the Genetic Algorithm (GA) to automate the composing of a detecting module that consists of a cohesion metric type and a coupling metric type and passes its resulting paired value to a deep learning approach to automate the detection of the large class bad smell. The accuracy that has been gained from using this approach reached 94.21%.