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Category Archives: Research
Paper on stream parallelization refactoring accepted at ICSE 2019
Our new paper entitled, “Safe Automated Refactoring for Intelligent Parallelization of Java 8 Streams” has been accepted to the International Conference on Software Engineering (ICSE) 2019 technical track! Out of 529 submissions, 109 papers were accepted (an acceptance rate of 20.6%). The conference will be held in Montréal later next year. An abstract is listed below. (more…)
New bachelors student Walee Ahmed joins the team
A new bachelors student, Walee Ahmed, joins our team this Fall semester! Walee is a Hunter College Computer Science student set to graduate in 2019. He has already finished a BA in psychology! He also really enjoys various hackathons around the northeast. Welcome, Walee!
Preprint of our IEEE SCAM ’18 paper on stream refactoring now available
A preprint of our upcoming IEEE SCAM 2018 paper entitled, “A Tool for Optimizing Java 8 Stream Software via Automated Refactoring” is now available.
Paper accepted at IEEE SCAM 2018
Our paper entitled, “A Tool for Optimizing Java 8 Stream Software via Automated Refactoring,” has been accepted in the Engineering Track of the 18th IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2018), to be held in Madrid, Spain in September. An abstract of the paper is listed below: (more…)
ICSE 2018 poster now available
Our ICSE 2018 poster entitled, “Towards safe refactoring for intelligent parallelization of Java 8 Streams,” is now available.
Received PSC-CUNY Enhanced Research Award
I am pleased to announce that I have recently received a PSC-CUNY Enhanced Research Award for a project entitled, “Analyses and Automated Refactorings for Imperative Programs that Use Functional Features.” The award amount is $12,000 and will help support students and travel. The award program is an internal funding mechanism to help promote research at CUNY. A brief abstract of the proposal is listed below:
Imperative programming uses statements to alter a program’s state, whereas functional programming avoids mutating existing data. With the recent popularity rise of functional programming, imperative languages are increasingly incorporating new functional features, enabling developers not previously familiar with functional programming to enjoy many of its benefits. Despite the advantages, however, issues arise from the interplay between the two paradigms, particular regarding involving MapReduce-style operations. This project will address these problems by formulating a theoretical foundation for the analysis and refactoring of hybrid functional/imperative programs and subsequently used to identify code that may safely be refactored for performance gains. Based on typestate analysis, it will determine when it is advantageous and safe to run hybrid code in parallel via a novel ordering inference approach that the PI will introduce. This work will advance the state-of-the-art in program analysis and automated refactoring for this mixed paradigm.