69 citations · 86 across the 11 of their papers we have counts for
11 papers
From Discussion to Execution: Replicating Buggy and Correct Data Science Code
Ragib Shahariar Ayon, Mohammad Wardat, Shibbir Ahmed
Reproducing reliable data science code from informal sources is challenging due to ambiguous problem specifications, missing dependencies, and performance bottlenecks. Although dev…
SelfHeal: Empirical Fix Pattern Analysis and Bug Repair in LLM Agents
Niful Islam, Muhammad Anas Raza, Mohammad Wardat
Large Language Models (LLMs) have transformed software development and AI applications. While LLMs are designed for text processing, LLM agents extend this capability by enabling a…
When Agents Fail: A Comprehensive Study of Bugs in LLM Agents with Automated Labeling
Niful Islam, Ragib Shahriar Ayon, Deepak George Thomas +2
Large Language Models (LLMs) have revolutionized intelligent application development. While standalone LLMs cannot perform any actions, LLM agents address the limitation by integra…
Mock Deep Testing: Toward Separate Development of Data and Models for Deep Learning
Ruchira Manke, Mohammad Wardat, Foutse Khomh +1
While deep learning (DL) has permeated, and become an integral component of many critical software systems, today software engineering research hasn't explored how to separately te…
Leveraging Data Characteristics for Bug Localization in Deep Learning Programs
Ruchira Manke, Mohammad Wardat, Foutse Khomh +1
Deep Learning (DL) is a class of machine learning algorithms that are used in a wide variety of applications. Like any software system, DL programs can have bugs. To support bug lo…
muPRL: A Mutation Testing Pipeline for Deep Reinforcement Learning based on Real Faults
Deepak-George Thomas, Matteo Biagiola, Nargiz Humbatova +4
Reinforcement Learning (RL) is increasingly adopted to train agents that can deal with complex sequential tasks, such as driving an autonomous vehicle or controlling a humanoid rob…
An Effective Data-Driven Approach for Localizing Deep Learning Faults
Mohammad Wardat, Breno Dantas Cruz, Wei Le +1
Deep Learning (DL) applications are being used to solve problems in critical domains (e.g., autonomous driving or medical diagnosis systems). Thus, developers need to debug their s…
Characterizing Bugs in Python and R Data Analytics Programs
Shibbir Ahmed, Mohammad Wardat, Hamid Bagheri +2
R and Python are among the most popular languages used in many critical data analytics tasks. However, we still do not fully understand the capabilities of these two languages w.r.…
DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs
Mohammad Wardat, Breno Dantas Cruz, Wei Le +1
Deep Neural Networks (DNNs) are used in a wide variety of applications. However, as in any software application, DNN-based apps are afflicted with bugs. Previous work observed that…
The Art and Practice of Data Science Pipelines: A Comprehensive Study of Data Science Pipelines In Theory, In-The-Small, and In-The-Large
Sumon Biswas, Mohammad Wardat, Hridesh Rajan
Increasingly larger number of software systems today are including data science components for descriptive, predictive, and prescriptive analytics. The collection of data science s…
DeepLocalize: Fault Localization for Deep Neural Networks
Mohammad Wardat, Wei Le, Hridesh Rajan
Deep neural networks (DNNs) are becoming an integral part of most software systems. Previous work has shown that DNNs have bugs. Unfortunately, existing debugging techniques do not…