#Bordertool name doesnt match software
#Bordertool name doesnt match code

#Bordertool name doesnt match code

Generic pipeline model is the most recent code clone detection that comprises five processes which are parsing process, pre-processing process, pooling process, comparing processes and filtering process to detect code clone. Thus, this scenario makes it more difficult to detect code clones.

#Bordertool name doesnt match software

As software grows and becomes a legacy system, the complexity of these approaches in detecting code clones increases.

Many approaches such as textual based comparison approach, token based comparison and tree based comparison approach have been used to detect code clones. Current code clone research focuses on the detection and analysis of code clones in order to help software developers identify code clones in source codes and reuse the source codes in order to decrease the maintenance cost. We found that seven of these programming rules were alternate codes.Ĭode clone is known as identical copies of the same instances or fragments of source codes in software. We extracted 22 programming rules by our method. We evaluated the method with various JavaScript code, including frameworks, libraries, and user code gathered from the Web. These programming rules are the alternate codes. Furthermore, we extract programming rules by using data mining in alternate code candidates. Our method is based on this assumption and extracts alternate code candidates by using the syntax tree containing the control structure in the JavaScript code repository. In a JavaScript Program with compatibility, we assume that alternate codes have a control structure based on conditional branching for object judgment.

We also describe a JavaScript code repository, which can be utilized to find alternate codes. We propose a method for finding alternate codes that can absorb environmental dependencies by crawling, gathering, converting, and mining JavaScript code from theWeb. When developers implement JavaScript systems, they must allow for differences among the various environments in which the JavaScript will be executed. JavaScript is used as a client-side language in Web applications. We show the usefulness of the guided fault seeding with the help of a case study using the blackboard application. In this work, we study fault seeding mechanisms based on user interactions with the application, and thus give a guided fault seeding mechanism for the purpose. This could lead to more effective fault-seeding in a test application. We argue that if the intended usage of the application under test could be inferred from the potential users' interactions with the application, such information could be incorporated into the fault-seeding process. One of the issues with fault seeding is the identification of potential areas in the application, where the faults are to be seeded. This is helpful in establishing confidence in the test suite and is an alternative to structural testing methods. if a given test suite is capable of uncovering the injected faults, of a test suite.

Fault Seeding is a testing technique where faults are artificially injected into an application to assess the effectiveness, i.e.