212 citations · 810 across the 54 of their papers we have counts for
29 papers · 1 filter
TRACED: Execution-aware Pre-training for Source Code
Yangruibo Ding, Ben Steenhoek, Kexin Pei +3
Most existing pre-trained language models for source code focus on learning the static code text, typically augmented with static code structures (abstract syntax tree, dependency…
CONCORD: Clone-aware Contrastive Learning for Source Code
Yangruibo Ding, Saikat Chakraborty, Luca Buratti +4
Deep Learning (DL) models to analyze source code have shown immense promise during the past few years. More recently, self-supervised pre-training has gained traction for learning…
Automated Code Editing with Search-Generate-Modify
Changshu Liu, Pelin Cetin, Yogesh Patodia +3
Code editing is essential in evolving software development. Many automated code editing tools have been proposed that leverage both Information Retrieval-based techniques and Machi…
TraceFixer: Execution Trace-Driven Program Repair
Islem Bouzenia, Yangruibo Ding, Kexin Pei +2
When debugging unintended program behavior, developers can often identify the point in the execution where the actual behavior diverges from the desired behavior. For example, a va…
CARGO: AI-Guided Dependency Analysis for Migrating Monolithic Applications to Microservices Architecture
Vikram Nitin, Shubhi Asthana, Baishakhi Ray +1
Microservices Architecture (MSA) has become a de-facto standard for designing cloud-native enterprise applications due to its efficient infrastructure setup, service availability,…
Automatic Map Generation for Autonomous Driving System Testing
Yun Tang, Yuan Zhou, Kairui Yang +5
High-definition (HD) maps are essential in testing autonomous driving systems (ADSs). HD maps essentially determine the potential diversity of the testing scenarios. However, the c…
VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements
Yangruibo Ding, Sahil Suneja, Yunhui Zheng +4
Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers' debugging efforts. This becomes even more important in…
Predictive Synthesis of API-Centric Code
Daye Nam, Baishakhi Ray, Seohyun Kim +2
Today's programmers, especially data science practitioners, make heavy use of data-processing libraries (APIs) such as PyTorch, Tensorflow, NumPy, Pandas, and the like. Program syn…
A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation
Ziyuan Zhong, Yun Tang, Yuan Zhou +3
Automated Driving Systems (ADSs) have seen rapid progress in recent years. To ensure the safety and reliability of these systems, extensive testings are being conducted before thei…
Retrieval Augmented Code Generation and Summarization
Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty +2
Software developers write a lot of source code and documentation during software development. Intrinsically, developers often recall parts of source code or code summaries that the…
Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles
Ziyuan Zhong, Gail Kaiser, Baishakhi Ray
Self-driving cars and trucks, autonomous vehicles (AVs), should not be accepted by regulatory bodies and the public until they have much higher confidence in their safety and relia…
On Multi-Modal Learning of Editing Source Code
Saikat Chakraborty, Baishakhi Ray
In recent years, Neural Machine Translator (NMT) has shown promise in automatically editing source code. Typical NMT based code editor only considers the code that needs to be chan…
CADET: Debugging and Fixing Misconfigurations using Counterfactual Reasoning
Rahul Krishna, Md Shahriar Iqbal, Mohammad Ali Javidian +2
Modern computing platforms are highly-configurable with thousands of interacting configurations. However, configuring these systems is challenging. Erroneous configurations can cau…
Understanding Local Robustness of Deep Neural Networks under Natural Variations
Ziyuan Zhong, Yuchi Tian, Baishakhi Ray
Deep Neural Networks (DNNs) are being deployed in a wide range of settings today, from safety-critical applications like autonomous driving to commercial applications involving ima…
Deep Learning & Software Engineering: State of Research and Future Directions
Prem Devanbu, Matthew Dwyer, Sebastian Elbaum +6
Given the current transformative potential of research that sits at the intersection of Deep Learning (DL) and Software Engineering (SE), an NSF-sponsored community workshop was co…
Deep Learning based Vulnerability Detection: Are We There Yet?
Saikat Chakraborty, Rahul Krishna, Yangruibo Ding +1
Automated detection of software vulnerabilities is a fundamental problem in software security. Existing program analysis techniques either suffer from high false positives or false…
Patching as Translation: the Data and the Metaphor
Yangruibo Ding, Baishakhi Ray, Premkumar Devanbu +1
Machine Learning models from other fields, like Computational Linguistics, have been transplanted to Software Engineering tasks, often quite successfully. Yet a transplanted model'…
Pythia: Grammar-Based Fuzzing of REST APIs with Coverage-guided Feedback and Learning-based Mutations
Vaggelis Atlidakis, Roxana Geambasu, Patrice Godefroid +2
This paper introduces Pythia, the first fuzzer that augments grammar-based fuzzing with coverage-guided feedback and a learning-based mutation strategy for stateful REST API fuzzin…
MTFuzz: Fuzzing with a Multi-Task Neural Network
Dongdong She, Rahul Krishna, Lu Yan +2
Fuzzing is a widely used technique for detecting software bugs and vulnerabilities. Most popular fuzzers generate new inputs using an evolutionary search to maximize code coverage.…
A Transformer-based Approach for Source Code Summarization
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray +1
Generating a readable summary that describes the functionality of a program is known as source code summarization. In this task, learning code representation by modeling the pairwi…
Rebuttal to Berger et al., TOPLAS 2019
Baishakhi Ray, Prem Devanbu, Vladimir Filkov
Berger et al., published in TOPLAS 2019, is a critique of our 2014 FSE conference abstract and its archival version, the 2017 CACM paper: A Large-Scale Study of Programming Languag…
Testing DNN Image Classifiers for Confusion & Bias Errors
Yuchi Tian, Ziyuan Zhong, Vicente Ordonez +2
Image classifiers are an important component of today's software, from consumer and business applications to safety-critical domains. The advent of Deep Neural Networks (DNNs) is t…
CODIT: Code Editing with Tree-Based Neural Models
Saikat Chakraborty, Yangruibo Ding, Miltiadis Allamanis +1
The way developers edit day-to-day code tends to be repetitive, often using existing code elements. Many researchers have tried to automate repetitive code changes by learning from…
A Case Study on the Impact of Similarity Measure on Information Retrieval based Software Engineering Tasks
Md Masudur Rahman, Saikat Chakraborty, Gail Kaiser +1
Information Retrieval (IR) plays a pivotal role in diverse Software Engineering (SE) tasks, e.g., bug localization and triaging, code retrieval, requirements analysis, etc. The cho…