International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 7 (2018) pp. 5167-5174 © Research India Publications. http://www.ripublication.com 5167 Test Case Retrieval Model by Genetic Algorithm with UML Diagram Itti Hooda (1), , Prof. Dr. Rajender Singh Chhillar (2) 1 Department of Computer Science and Applications, Maharishi Dayanand University, Rohtak,(Haryana), India. 2 Department of Computer Science and Applications, Maharishi Dayanand University, Rohtak,(Haryana), India. Abstract In this research paper we have shown the Reusability of test cases. If companies make repositories of Test cases then they can automatically extract the cases from Database, but the problem is matching of the test cases and the new Project UML Design. So we have used optimize weight model by Genetic algorithm which match the test cases by cosine similarity and extract the High Cosine similarity test cases which analysis on the basis of TPR and FPR (True positive rate and False positive rate). We can also find the accuracy of proposed process. We achieve significance high accuracy like in case of ATM 83%, Student management System 81.23% and Banking System 80.23%. Keywords: FPR, IF Methods, LDA, BLUiR,UML, Mutation,Crossover,GA. INTRODUCTION Modern society depends on software-intensive systems. Software operates intangibly or very quietly in everything from kitchen appliances to critical infrastructure, and one's living a life exclusive of every day relying on systems running software needs a determined downshifting from life as most people enjoy it. As the importance of software constantly grows, so does the significance of being able to create it proficiently. Software development is an wide-ranging expression used to illustrate any approach to produce source code and its correlated documentation. Through the software crisis of the 1960s, it became clear that software difficulty quickly rises when software are scaled up to larger systems. The development techniques that were applied at the time did not result in needed software in a predictable manner. [7] A powerful search engine that can retrieve software artifacts with high precision is obviously of great importance to developers. Software systems usually contain defects that need to be fixed after releases, and in some projects users are allowed to submit feedback on these defects that they encounter through bug reporting systems such as Bugzilla. Owing to this inherent complexity of software construction, software bugs remain frequent. For a large software system, the number of bugs may range from hundreds to thousands. However, performing this process manually for lot of bugs is time consuming and exclusive. Consequently, efficient techniques for locating bugs automatically from bug reports are highly desirable [1].In recent years, information retrieval (IR) based bug localization techniques have gained significant attention owing to their relatively low computational cost and minimal external dependencies. Effectively supporting software changes is essential to give a sustainable high-quality evolution of large-scale software systems, as realizing even a slight change may not be always straightforward. Software- change impact analysis, or basically impact analysis (IA), has been acknowledged as one such key protection activity. IA aims at estimating the potentially impacted entities of a system due to a proposed change [5]. The goal of contextual search is to incorporate a richer model of human searching behaviour into search systems, and as such, represents an opportunity for collaboration between information behaviour (IB) and information retrieval (IR), two fields which have hitherto progressed mostly in parallel. Researchers have worked towards maximizing the impact that software engineering research has on practice, for example, by providing methods and outputs that are as general (and thus as useful) as possible. A significant amount of research on applying Information Retrieval (IR) methods for analysing textual information in software artifacts has been conducted in the SE community in recent years. Software testing is indispensable for all software development. It is an integral part of the software engineering discipline. However, testing is labour-intensive and exclusive. It often accounts for more than 50% of total development costs. Thus, there are clear advantage in reducing the cost and improving the efficiency of software testing by automating the process. [9]. In fact, there has been a quick expansion of practices in using automated software testing tools. Currently, a large number of software test automation tools have been developed and have become accessible on the market. Between a range of testing activities, test case invention is one of the most intellectually demanding tasks and it is also of the most critical challenges, since it can have a tough impact on the effectiveness and efficiency of the whole testing process The goal of performance testing is to find performance problems, when an application under test (AUT) unexpectedly exhibits worsened characteristics for a specific workload. Existing feature location techniques use different tactics to find a feature’s source code. Approaches based on information retrieval (IR) leverage the fact that identifiers knowledge to locate source code that is textually similar to a query describing a feature is there [4]. Various researchers have applied information retrieval methods to automatically search for appropriate files based on bug reports. They treat an initial bug report as a query and rank the source code files by their significance to the query. The developers can then analysis the returned files and fix the bug. These techniques are information retrieval based bug localization methods. Numerous of the obtainable IR-based