Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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Subject: search.filters.subject.Automate

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    DevFlair: A Framework to Automate the Pre-screening Process of Software Engineering Job Candidates
    (IEEE, 2022-12-09) Jayasekara, R.T.R; Kudarachchi, K.A.N.D; Kariyawasam, K.G.S.S.K; Rajapaksha, D; Jayasinghe, S.L; Thelijjagoda, S
    The HR department of a technology company receives hundreds of job applications for each Software Engineering related vacancy. Evaluating a candidate by looking at the curriculum vitae may appear to be easy during the pre-screening process. However, an automated pre-screening process using Natural Language Processing and Machine Learning methodologies would help the recruiter to obtain a more accurate and deeper understanding of the candidate. In this paper we propose “DevFlair”, a framework to automate pre-screening Software Engineering job candidates. DevFlair uses data from social media, GitHub, and open-ended questionnaires to predict the Big-Five personality traits, analyze technical skill expertise, and analyze the experience in using industry-related online platforms. After analysis, the candidates are ranked according to their personality and technical skill levels. We conduct the personality prediction experiments using a social media posts dataset annotated with gold-standard Big-Five personality labels. We train FastText classification models and compare their accuracy against other state of the art classification models. The comparisons conclude that the FastText classification models substantially outperform the state of the art classification models when predicting Openness, Conscientiousness, and Agreeableness personality traits.
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    Automate Traditional Interviewing Process Using Natural Language Processing and Machine Learning
    (IEEE, 2021-04-02) Senarathne, P; Silva, M; Methmini, A; Kavinda, D; Thelijjagoda, S
    Nowadays, almost everything is equipped with technology. People can save time by using modern day technological applications in the most convenient way. Smart Interviewing System is one such software/tool which automates the traditional interviewing process using modern Natural Language Processing techniques and deep learning applications. The system will be mainly beneficial for interviewers and HR management employees working for different organizations who conduct technology related interviews. The system works with human voice and writing patterns. The system converts human language into system understandable text-based inputs, and these are used as inputs in the automated interviewing process. The system then checks the accuracy of the answers which candidates provided on the both oral interviews/ technical interviews and written tests. Later, the system automatically predicts scores for each answer using concepts of the deep learning. Interviewers can reduce the effort that they have to put in for selecting the most suitable candidates who are qualified enough to work with their organization. SIS is developed based on modern DL and NLP concepts using Python programming language alongside with ReactJS Framework. This system checking and evaluating candidate more accurately in every stage of the interview using advance evaluation parameters than human oriented evaluations. Above process lead system to find more human errors which critically can be affected to future of the organizations. Because of that, it can be led organizations to find best human resources comparing to the traditional interviewing process by sacrificing less time and effort.
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    An ontology-based approach to automate the software development process
    (IEEE, 2018-12-21) Athiththan, K; Rovinsan, S; Sathveegan, S; Gunasekaran, N; Gunawardena, K. S. A. W; Kasthurirathna, D
    Ontologies are well-known for representing knowledge of a particular domain and an upcoming trend in the field of Computer Science to produce intelligent systems. Further, they help to solve traceability issues and transitive dependencies. Creation of software applications and use of software applications are increased due to new and innovative customer requirements and technologies. Development teams are following several Software Development methodologies to support and to produce quality software applications to the outer world. Agile methodologies are becoming more popular in small organizations and development teams to support the feature by feature development with less throughput. User Stories represent the actual user requirements in Agile. This paper proposes a solution named Sponto, which to automate the software development process using ontology-based approaches to produce reliable boilerplates to the developers and the communities to reuse the base and to build the software applications without spending a considerable amount of time on recreating the artifacts. The proposed solution supports and generates database scripts, Business Process Model diagrams, Java code snippets, and test cases from user stories.