Discovering Program Topoi via Hierarchical Agglomerative Clustering - 2018


In long lifespan software systems, specification documents will be outdated or even missing. Developing new software releases or checking whether some user needs are still valid becomes challenging in this context. This challenge can be addressed by extracting high-level observable capabilities of a system by mining its source code and also the available source-level documentation. This Project presents feature extraction and traceability (FEAT), an approach that automatically extracts topoi , that are summaries of the most capabilities of a program, given below the form of collections of code functions along with an index . FEAT acts in 2 steps: first, clustering: by mining the accessible source code, probably augmented with code-level comments, hierarchical agglomerative clustering teams similar code functions. Moreover, this process gathers an index for each function. Second, entry point selection: functions within a cluster are then ranked and presented to validation engineers as topoi candidates. We implemented FEAT on top of a general-purpose take a look at management and optimization platform and performed an experimental study over fifteen open-supply software comes amounting to a lot of than one M lines of codes proving that automatically discovering topoi is possible and meaningful on realistic projects.

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