Shipra Sharma

Ritu Kapur, Balwinder Sodhi, Poojith U Rao et al.

Code reviews are one of the effective methods to estimate defectiveness in source code. However, the existing methods are dependent on experts or inefficient. In this paper, we improve the performance (in terms of speed and memory usage) of our existing code review assisting tool--CRUSO. The central idea of the approach is to estimate the defectiveness for an input source code by using the defectiveness score of similar code fragments present in various StackOverflow (SO) posts. The significant contributions of our paper are i) SOpostsDB: a dataset containing the PVA vectors and the SO posts information, ii) CRUSO-P: a code review assisting system based on PVA models trained on \emph{SOpostsDB}. For a given input source code, CRUSO-P labels it as {Likely to be defective, Unlikely to be defective, Unpredictable}. To develop CRUSO-P, we processed >3 million SO posts and 188200+ GitHub source files. CRUSO-P is designed to work with source code written in the popular programming languages {C, C#, Java, JavaScript, and Python}. CRUSO-P outperforms CRUSO with an improvement of 97.82% in response time and a storage reduction of 99.15%. CRUSO-P achieves the highest mean accuracy score of 99.6% when tested with the C programming language, thus achieving an improvement of 5.6% over the existing method.

Shipra Sharma, Balwinder Sodhi

Domain-specific knowledge graphs constructed from natural language text are ubiquitous in today's world. In many such scenarios the base text, from which the knowledge graph is constructed, concerns itself with practical, on-hand, actual or ground-reality information about the domain. Product documentation in software engineering domain are one example of such base texts. Other examples include blogs and texts related to digital artifacts, reports on emerging markets and business models, patient medical records, etc. Though the above sources contain a wealth of knowledge about their respective domains, the conceptual knowledge on which they are based is often missing or unclear. Access to this conceptual knowledge can enormously increase the utility of available data and assist in several tasks such as knowledge graph completion, grounding, querying, etc. Our contributions in this paper are twofold. First, we propose a novel Markovian stochastic model for document generation from conceptual knowledge. The uniqueness of our approach lies in the fact that the conceptual knowledge in the writer's mind forms a component of the parameter set of our stochastic model. Secondly, we solve the inverse problem of learning the best conceptual knowledge from a given document, by finding model parameters which maximize the likelihood of generating the specific document over all possible parameter values. This likelihood maximization is done using an application of Baum-Welch algorithm, which is a known special case of Expectation-Maximization (EM) algorithm. We run our conceptualization algorithm on several well-known natural language sources and obtain very encouraging results. The results of our extensive experiments concur with the hypothesis that the information contained in these sources has a well-defined and rigorous underlying conceptual structure, which can be discovered using our method.