Ali Reza Ibrahimzada

I am a CS PhD student in the Department of Computer Science at the University of Illinois Urbana-Champaign, IL, United States. My research interests include LLMs for Code, Software Testing, Program Analysis, Model Interpretation, and Deep Learning. Moreover, I am a member of the Intelligent CAT Lab advised by Prof. Reyhaneh Jabbarvand since Fall 2022.

I will be a Visiting Researcher at IBM Research - IBM T.J. Watson Research Center, Yorktown Heights, NY, United States in Summer 2023. I will be working on applications of LLMs for Code in the PLSE group.

news

Apr 21, 2023	Ray Ozzie Computer Science Fellowship
Sep 7, 2022	Valedictorian of Class of 2022
Jun 14, 2022	SEER accepted to ESEC/FSE 2022
May 12, 2021	Research Internship at University of Illinois Urbana-Champaign

internships

IBM Research

IBM T.J. Watson Research Center, Yorktown Heights, NY Summer 2023

talks

Perfect Is the Enemy of Test Oracle

ESEC/FSE 2022, National University of Singapore November 2022

selected publications

FSE’22

Perfect Is the Enemy of Test Oracle

Ibrahimzada, Ali Reza, Varli, Yigit, Tekinoglu, Dilara, and Jabbarvand, Reyhaneh

In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE ’22), November 14–18, 2022, Singapore, Singapore

Abs arXiv DOI PDF Code Slides Video

Automation of test oracles is one of the most challenging facets of software testing, but remains comparatively less addressed compared to automated test input generation. Test oracles rely on a ground-truth that can distinguish between the correct and buggy behavior to determine whether a test fails (detects a bug) or passes. What makes the oracle problem challenging and undecidable is the assumption that the ground-truth should know the exact expected, correct or buggy behavior. However, we argue that one can still build an accurate oracle without knowing the exact correct or buggy behavior, but how these two might differ. This paper presents SEER, a Deep Learning-based approach that in the absence of test assertions or other types of oracle, can automatically determine whether a unit test passes or fails on a given method under test (MUT). To build the ground-truth, SEER jointly embeds unit tests and the implementation of MUTs into a unified vector space, in such a way that the neural representation of tests are similar to that of MUTs they pass on them, but dissimilar to MUTs they fail on them. The classifier built on top of this vector representation serves as the oracle to generate “fail” labels, when test inputs detect a bug in MUT or “pass” labels, otherwise. Our extensive experiments on applying SEER to more than 5K unit tests from a diverse set of opensource Java projects show that the produced oracle is (1) effective in predicting the fail or pass labels, achieving an overall accuracy, precision, recall, and F1 measure of 93%, 86%, 94%, and 90%, (2) generalizable, predicting the labels for the unit test of projects that were not in training or validation set with negligible performance drop, and (3) efficient, detecting the existence of bugs in only 6.5 milliseconds on average. Moreover, by interpreting the neural model and looking at it beyond a closed-box solution, we confirm that the oracle is valid, i.e., it predicts the labels through learning relevant features.