Sk Adnan Hassan - Research

SEA (Software Engineering Analytics) Lab, Virginia Tech

Graduate Research Assistant (Aug 2019- July 2022), Advisor: Dr. Francisco Servant
I worked on a project related to ReDoS Comprehension which resulted in an accepted paper in S&P'2023.

Many regex engines built into the current generation of programming languages today (e.g., PHP, Perl, Java, JavaScript, etc.) rely on Spencer's backtracking based algorithm. Unfortunately, these engines expose applications to an understudied denial of-service attack vector called Regular Expression Denial of Service (ReDoS). We are trying address the four main problems that contribute to the unsafety of regexes: the difficulty of composing safe (non-SL) regexes, the difficulty of fixing unsafe (SL) regexes, the limitation (SL runtime) of regex engines, and the unsafety of some extended features.

SEL Lab, Washington State University

Remote Researcher (Oct 2018- March 2019), Advisor: Dr. Venera Arnaoudova
I worked on project related to Program Comprehension which resulted in an accepted paper in ICPC'2019

There are several widely accepted metrics to measure code quality that are currently being used in both research and practice to detect code smells and to find opportunities for code improvement. Previous research results show that there may be inconsistencies between, on the one hand, the results from metrics for cohesion, coupling, complexity, and readability, and, on the other hand, the interpretation of these metrics in practice. In this study, we investigate 548 instances of source code readability improvements, as explicitly stated by internal developers in practice, from 63 engineered software projects. We show that current readability models fail to capture readability improvements. We also show that tools to calculate additional metrics, to detect refactorings, and to detect style problems are able to capture characteristics that are specific to readability changes and thus should be considered by future readability models.

Software Engineering Lab, Bangladesh University of Engineering and Technology

Undergraduate Researcher, Advisor: Dr. Anindya Iqbal & Dr. Amiangshu Bosu
I worked on this project as part of my undergraduate thesis. This work resulted in an accepted paper in APSEC'2018

In this research we performed standard empirical studies to find out the correlation between question difficulty and factors like the votes on accepted answer, views received, and resolution time of the questions and found that Basic questions have higher view counts, higher upvotes, and lower answer interval( i.e, time to receive the accepted answer) than Intermediate and Advanced questions. We have identified textual and contextual features of a question and divided them into two categories: pre-hoc and post-hoc features. We observed a model based on Random Forest achieving highest mean accuracy (67.6%), using only answer-independent pre-hoc features. Accommodating answer-dependent post-hoc features, we were able to improve the mean accuracy to 75.2%.