In today’s challenging world software testing is a very crucial part of the software development lifecycle (SDLC) as it helps in minimizing errors, and reduces maintenance and cost of the software. This work aims to analyze the various tools presently available for software automation. The selection of productive testing tools plays a vital role in the implementation of software products with high quality and it also ensures premium quality throughout the SDLC. One of the key issues is the selection of adequate software testing tools and frameworks. This paper discusses Quick Test Professional (QTP) and Selenium on the basis of various attributes which include Source and Licensing, Testing Cost, Object Repository, Usability, and programming language support.The use of an adequate automated software tool provides ease in testing and allows the tester to execute test cases in an efficient manner by overcoming the challenges such as limited time, increased pressure and minimum resources.

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Machine learning, a class of artificial intelligence (AI), is widely used for big data analytics. It is now vastly used for accomplishing the Robot Navigation task by simply applying different algorithms. These algorithms convert user-generated commands into machine-understandable language. This is done by a wall-following control that is a robot’s movements in arbitrary directions while maintaining a specific distance from a particular wall. This paper illustrates two leading research contributions. Firstly, it discusses the significance of ML models in Robot navigations. Secondly, this paper comprises a detailed study and comparative analysis on the execution of different ML and Deep Learning algorithms using all three robot navigation formats (short, full, and simpler). In this paper, the evaluations and assessments of all the models are done by Monte-Carlo cross-validation.

The research article describes a system of machine architecture for protection of transmission lines of HVDC, in which multiple models of ML (KNN and SVM) are employed for fault classification and recognition. The K-Nearest Neighbor classifier is intended to serve two functions. It detects the type of fault as well which serves as a backup module for the starting unit's doubtful fault declaration. From a single-end single measurement, a feature vector consisting of standard deviations gradients and of DC and harmonic current, DC voltage, and correlation coefficients is retrieved. By simulating different states that are non-fault and fault states on a data set having training and test cases are obtained. The ML algorithm is trained in MATLAB and evaluated on a total of 2220 severe instances. The acquired results demonstrated the efficacy of the suggested method in detecting and distinguishing between various internal and external/pseudo problems.In this paper we will discuss how the ANN model Simulink in the MATLAB is used for researching, collecting, and evaluating 456 data sets.