Wipro Limited

Varun Anant

Aman Chandra

The present disclosure is related in general to software testing and a method and a system for automatically identifying violation in the test cases. A test case validation system categorizes the test cases into event-based test cases and binary test cases. Further, a Part-Of-Speech (POS) pattern is detected in the one or more test cases based on POS tags assigned to each of the tokens in test cases. Thereafter, comparison of the detected POS pattern and the one or more tokens with predefined POS patterns and predefined tokens identifies violations in the one or more test cases if any, using pattern matching and Natural Language Processing (NLP). The predefined POS patterns and tokens used for comparison are filtered based on category of the test case thus accelerating the process of the violation identification. The test case validation system is capable of accurately identifying more than one type of violations simultaneously.

Method and a system for automatically identifying violations in one or more test cases

This disclosure relates generally to software development, and more particularly to a system and method for representing software development requirements into standard diagrams. In one embodiment, a method is provided for deriving use case diagrams for software development. The method comprises extracting one or more core components from each of a plurality of software development requirements, determining at least one activity from the one or more core components, extracting at least one of a class and an attribute from the one or more core components, and deriving at least one use case diagram based on the at least one activity and the at least one of the class and the attribute. The one or more core components comprise at least one of a core message and a condition.

System and method for representing software development requirements into standard diagrams

System and method for validating software development requirements are disclosed. The method comprises accessing a software development requirement, extracting a plurality of tokens from the software development requirement, tagging each of the plurality of tokens to a corresponding part of speech, and deriving a pattern based on the plurality of tokens and the plurality of corresponding parts of speech. The method further comprises determining at least one of a context and a business domain of the software development requirement and identifying pre-defined rules for the plurality of tokens and pre-defined patterns for the pattern. In response to a positive identification, the software development requirement are validated by analyzing the plurality of tokens against the pre-defined rules and by analyzing the pattern against the pre-defined patterns. In response to a negative identification, a learning process is initiated based on intelligence gathered from a manual validation of the software development requirement.

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Bengaluru, India

Varun Anant