Integrating Refactoring Recommendation into an IDE: A JetBrains Story
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In software engineering, a great number of new approaches are being actively researched, and a lot of tools are being developed based on them. These tools require a framework for their creation and an opportunity to be used by potential developers. Modern IDEs provide both. In this paper, we describe the main capabilities of the IntelliJ Platform that could be useful for researchers that are developing code analysis tools. To illustrate the benefits of using the platform, we describe several use cases that researchers might be interested in: mining software data, running machine learning models on code, recommending refactorings, and visualizing data in the IDE. We provide several examples of existing plugins that implement these cases. Finally, to make it easier to start working with the platform, we develop and provide simple plugins for each use case that could serve as a template for a new project.
Pre-printIn this talk, I will first present the history of RefactoringMiner, how it was conceived, how it evolved, and what are the fundamental ideas behind it. I will talk about the refactoring mining promises and how they have been fulfilled, the novel research and tools that were enabled by the recent refactoring mining advancements, and the open problems that refactoring mining can help to solve in the future.
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A Software Refactoring Community Infrastructure
Dr. Marouane Kessentini is currently a tenured associate professor at the University of Michigan-Dearborn; founding director of the Dearborn AI Research Center (DAIR) and director of the NSF IUCRC center on Pervasive AI; Michigan Site. He received his Ph.D. from the University of Montreal in Canada in 2012. Dr. Kessentini is a recipient of the prestigious 2018 President of Tunisia distinguished research award, the University distinguished teaching award, the University distinguished digital education award, the College of Engineering and Computer Science distinguished research award, 4 best paper awards including an IEEE 10 Year Most Influential Paper award in Program Comprehension (2011-2021), and his AI-based software refactoring invention, licensed and deployed by Fortune 500 companies, and selected as one of the Top 8 inventions at the University of Michigan for 2018 among over 500 inventions, by the UM Technology Transfer Office. He received various grants from both industry and federal agencies and published over 160 papers in top journals and conferences. Dr. Kessentini has extensive collaborations with the industry on different areas related to refactoring, software engineering intelligence, search-based software engineering, Edge AI, AI/MLOps, AI and cyber-physical systems, intelligent software bots, etc. He is the co-founder of many workshops, General Chair of SSBSE16 and ASE22, and PC chair of MODELS19, SANER 2021, GECCO, etc. He served as a keynote speaker at various venues including ICSR, SSBSE, GECCO, WCCI, etc. He graduated 15+ Ph.D. students and served as associate editor in 7 journals and PC member of over 150 conferences.
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Cyber security is reliant on the actions of both machine and human and remains a domain of importance and continual evolution. While the study of human behavior has grown, less attention has been paid to the adversarial operator. Cyber environments consist of complex and dynamic situations where decisions are made with incomplete information. In such scenarios people form strategies based on simplified models of the world and are often efficient and effective, yet may result in judgement or decision-making bias. In this paper, we examine an initial list of biases affecting adversarial cyber actors. We use subject matter experts to derive examples and demonstrate these biases likely exist, and play a role in how attackers operate.
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While the energy efficiency of mobile apps is receiving considerable attention in recent years, Android developers have little tools to assess the energy footprint of their applications. In this paper, we introduce PowDroid, our tool to estimate the energy consumption of Android application. It uses system-wide metrics and does not require access to applications’ source code. We run PowDroid on a use-case scenario comparing the energy footprint of applications in different categories.
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