Registered user since Tue 2 May 2017
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Changes to a software project are inevitable as the software requires continuous adaptations, improvements, and corrections throughout maintenance. Identifying the purpose and impact of changes made to the codebase is critical in software engineering. However, manually identifying and characterizing software changes can be a time-consuming and tedious process that adds to the workload of software engineers. To address this challenge, several attempts have been made to automatically identify and demystify intents of software changes based on software artifacts such as commit change logs, issue reports, change messages, source code files, and software documentation. However, these existing approaches have their limitations. These include a lack of data, limited performance, and an inability to evaluate compound changes. This paper presents a doctoral research proposal that aims to automate the process of identifying commit-level changes in software projects using software repository mining and code representation learning models. The research background, state-of-the-art, research objectives, research agenda, and threats to validity are discussed.
Link to publication DOIContext: Serverless computing allows developers to create and deploy applications without the need to manage any underlying infrastructure, making it a more efficient and effective way to bring products to market. Serverless technology is gaining widespread adoption among a large number of companies, becoming increasingly popular. However, the adoption of serverless technology brings with it a number of new challenges. Objective: To this end, we plan to gain a deep understanding of challenges and strategies, architectural issues and their causes, QAs and tactics of serverless systems, architectural patterns and antipatterns, migration towards serverless architecture, and state-of-the-art practices for vendor lock-in problems. Methodology: The research objective will be met through the use of an industrial empirical approach, including interviews, a case study, and a questionnaire survey. Possible outcomes: The expected outcomes would be (i) a multivocal literature review on design areas of serverless architecture (ii) an evidence-based framework for synthesizing serverless architectural challenges/solutions (iii) a decision-making process for migrating to serverless architecture (iv) empirical investigations on QAs and tactics for serverless systems (v) a decision-making framework for serverless architecture.
