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Start date: 2022Subject: search.filters.subject.Software Engineering

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Now showing 1 - 3 of 3
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    CodeHarbor: A Code Analysis Tool
    (Springer Science and Business Media Deutschland GmbH, 2026) Dewmin T.Y; Kodithuwakku Y.S.; Dayananda I.H.M.B.L; Fernando K.R.A.W; De Silva D.I; Rathnayake S.
    As software systems grow more complex, developers face increasing challenges in maintaining and evolving codebases, often resulting in higher costs and longer development cycles. To address these issues, this study presents CodeHarbor, an intelligent tool that integrates machine learning with code analysis to simplify complex code segments. CodeHarbor calculates complexity metrics and offers personalized, context-aware suggestions for improving code quality. By automating code reviews, detecting anomalies, and recommending optimized refactoring strategies, it enables early issue resolution and enhances maintainability. The backend leverages artificial intelligence to identify patterns, enforce coding standards, and generate actionable insights, while the intuitive frontend provides real-time feedback, visualizations, and detailed improvement summaries. CodeHarbor also highlights repetitive patterns and compliance issues, helping developers track progress and reduce manual review effort. With its seamless integration of analysis and interface, CodeHarbor streamlines development workflows and promotes sustainable, high-quality software engineering.
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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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    Success Factors of Requirement Elicitation in the Field of Software Engineering
    (IEEE, 2022-12-09) Attanayaka, B; Nawinna, D; Manathunga, K; Abeygunawardhana, P. K. W
    Requirement elicitation (RE) is a cognitively challenging and time-consuming task in software development due to the numerous challenges associated with it including conflicting requirements, unspoken, or assumed requirements, difficulty meeting with relevant stakeholders, stakeholder resistance to change, and not enough time set aside for meetings with all stakeholders. The prime causes of software implementation failure have been identified as inadequacies in the treatment of requirements. Without collecting the quality requirement, cannot achieve the goal of a quality software product. Through identifying the success factors affecting requirement elicitation, the paths to the quality requirements can be identified. The success factors identify through this research are experience, business analyst skills, stakeholder relationship, organizational elicitation process. This study aims to identify the factors affecting requirement elicitation based on the data collected from business analysts and similar positions in the software industry through a survey, interviews, and analyzed data to provide the initial validation for the identified factors. Through the analysis, we identified the main factors affecting successful requirement elicitation with a perfect significance value of less than 0.05 for all factors.