Extracting Conceptual Knowledge from Natural Language Text Using Maximum Likelihood Principle

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.

APR: Architectural Pattern Recommender

This paper proposes Architectural Pattern Recommender (APR) system which helps in such architecture selection process. Main contribution of this work is in replacing the manual effort required to identify and analyse relevant architectural patterns in context of a particular set of software requirements. Key input to APR is a set of architecturally significant use cases concerning the application being developed. Central idea of APR's design is two folds: a) transform the unstructured information about software architecture design into a structured form which is suitable for recognizing textual entailment between a requirement scenario and a potential architectural pattern. b) leverage the rich experiential knowledge embedded in discussions on professional developer support forums such as Stackoverflow to check the sentiment about a design decision. APR makes use of both the above elements to identify a suitable architectural pattern and assess its suitability for a given set of requirements. Efficacy of APR has been evaluated by comparing its recommendations for "ground truth" scenarios (comprising of applications whose architecture is well known).

Estimating defectiveness of source code: A predictive model using GitHub content

Two key contributions presented in this paper are: i) A method for building a dataset containing source code features extracted from source files taken from Open Source Software (OSS) and associated bug reports, ii) A predictive model for estimating defectiveness of a given source code. These artifacts can be useful for building tools and techniques pertaining to several automated software engineering areas such as bug localization, code review, and recommendation and program repair. In order to achieve our goal, we first extract coding style information (e.g. related to programming language constructs used in the source code) for source code files present on GitHub. Then the information available in bug reports (if any) associated with these source code files are extracted. Thus fetched un(/ semi)-structured information is then transformed into a structured knowledge base. We considered more than 30400 source code files from 20 different GitHub repositories with about 14950 associated bug reports across 4 bug tracking portals. The source code files considered are written in four programming languages (viz., C, C++, Java, and Python) and belong to different types of applications. A machine learning (ML) model for estimating the defectiveness of a given input source code is then trained using the knowledge base. In order to pick the best ML model, we evaluated 8 different ML algorithms such as Random Forest, K Nearest Neighbour and SVM with around 50 parameter configurations to compare their performance on our tasks. One of our findings shows that best K-fold (with k=5) cross-validation results are obtained with the NuSVM technique that gives a mean F1 score of 0.914.

A Simplified Description of Fuzzy TOPSIS

A simplified description of Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) is presented. We have adapted the TOPSIS description from existing Fuzzy theory literature and distilled the bare minimum concepts required for understanding and applying TOPSIS. An example has been worked out to illustrate the application of TOPSIS for a multi-criteria group decision making scenario.