Inferring test cases from user bug reports'

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As part of recommended practice, projects employ issue trackers as a platform for communication between users and developers or contributors. This includes the request of new features or the report of issues and bugs. The information on these trackers is in a natural language and sometimes structured per a recommended format or template. However, it is yet untapped to guide the generation or repair of test cases. This project, in the first instance, aims to use this information to deduce action sequences that caused the bug to surface so that test cases can be generated for the confirmation of the issue, and later - the validation of the fix. To do so, techniques from natural language processing should be used for the following tasks: increase the quality of the collected data by recreating missing links between corpora, such as code commits and user bug reports - a task usually performed by a domain expert; classification of requests into features or bugs; using actor models to deduce action sequences when they are presented in a free-form narrative; use action sequences to generate or enhance test suits for the code under analysis;While NLP techniques have been used for the constructions of project ontologies and these in turn employed for creating links between software artefacts, these techniques have not been previously used directly.Ideally, this project should automate a typical task that occurs as part of the software life-cycle and thus should reduce the cost of software maintenance. It should also prompt other researchers to follow suit in tapping into unstructured information that exists in most projects using NLP techniques and should provide corpora of software artefacts in a linked data format for researchers to work with to tackle other questions than the ones considered here.'