Search Results for author:
Found 27 papers, 6 papers with code
On Explaining (Large) Language Models For Code Using Global Code-Based Explanations
Moreover, participants of this study highlighted Code$Q$'s ability to show a causal relationship between the input and output of the model with readable and informative explanations on code completion and test generation tasks.
Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives
To bring clarity to the current research status and identify opportunities for future work, we conducted a comprehensive review of $88$ papers: a systematic literature review of $18$ papers focused on LLMs in SE, complemented by an analysis of 70 papers from broader trust literature.
UniGenCoder: Merging Seq2Seq and Seq2Tree Paradigms for Unified Code Generation
Deep learning-based code generation has completely transformed the way developers write programs today.
SnipGen: A Mining Repository Framework for Evaluating LLMs for Code
SnipGen aims to mitigate data contamination by generating robust testbeds and crafting tailored data points to assist researchers and practitioners in evaluating LLMs for code-related tasks.
The ML Supply Chain in the Era of Software 2.0: Lessons Learned from Hugging Face
First, we evaluate the current state of documentation in the Hugging Face supply chain, report real-world examples of shortcomings, and offer actionable suggestions for improvement.
Toward Neurosymbolic Program Comprehension
Recent advancements in Large Language Models (LLMs) have paved the way for Large Code Models (LCMs), enabling automation in complex software engineering tasks, such as code generation, software testing, and program comprehension, among others.
How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study
These findings highlight the effectiveness of our benchmark in evaluating LLMs, providing valuable insights into their reliability and their propensity to introduce code smells in code generation tasks.
On Interpreting the Effectiveness of Unsupervised Software Traceability with Information Theory
In this paper, we propose an approach, TraceXplainer, for using information theory metrics to evaluate and better understand the performance (limits) of unsupervised traceability techniques.
Perspective of Software Engineering Researchers on Machine Learning Practices Regarding Research, Review, and Education
Objective: This study aims to contribute to the knowledge, about the synergy between ML and SE from the perspective of SE researchers, by providing insights into the practices followed when researching, teaching, and reviewing SE studies that apply ML.
Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Development
Despite the utility that Generative AI (GenAI) tools provide for tasks such as writing code, the use of these tools raises important legal questions and potential risks, particularly those associated with copyright law.
Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations
Trustworthiness and interpretability are inextricably linked concepts for LLMs.
Semantic GUI Scene Learning and Video Alignment for Detecting Duplicate Video-based Bug Reports
Video-based bug reports are increasingly being used to document bugs for programs centered around a graphical user interface (GUI).
Benchmarking Causal Study to Interpret Large Language Models for Source Code
We illustrate the insights of our benchmarking strategy by conducting a case study on the performance of ChatGPT under distinct prompt engineering methods.
Evaluating and Explaining Large Language Models for Code Using Syntactic Structures
To this end, this paper introduces ASTxplainer, an explainability method specific to LLMs for code that enables both new methods for LLM evaluation and visualizations of LLM predictions that aid end-users in understanding model predictions.
Toward a Theory of Causation for Interpreting Neural Code Models
To demonstrate the practical benefit of $do_{code}$, we illustrate the insights that our framework can provide by performing a case study on two popular deep learning architectures and ten NCMs.
An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation
This paper offers one of the first comprehensive empirical investigations into the connection between GUIs and functional, natural language descriptions of software.
It Takes Two to Tango: Combining Visual and Textual Information for Detecting Duplicate Video-Based Bug Reports
Given the importance of visual information to the process of identifying and understanding such bugs, users are increasingly making use of screenshots and screen-recordings as a means to report issues to developers.
Optical Character Recognition
Optical Character Recognition (OCR)
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Deep Learning & Software Engineering: State of Research and Future Directions
The intent of this report is to serve as a potential roadmap to guide future work that sits at the intersection of SE & DL.
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL).
Translating Video Recordings of Mobile App Usages into Replayable Scenarios
In light of unique mobile development constraints, including swift release cycles and rapidly evolving platforms, automated techniques for analyzing all types of rich software artifacts provide benefit to mobile developers.
Improving the Effectiveness of Traceability Link Recovery using Hierarchical Bayesian Networks
To this end, we design and implement a HierarchiCal PrObabilistic Model for SoftwarE Traceability (Comet) that is able to infer candidate trace links.
DeepMutation: A Neural Mutation Tool
To this aim, two characteristics of mutation testing frameworks are of paramount importance: (i) they should generate mutants that are representative of real faults; and (ii) they should provide a complete tool chain able to automatically generate, inject, and test the mutants.
On Learning Meaningful Code Changes via Neural Machine Translation
We show that, when applied in a narrow enough context (i. e., small/medium-sized pairs of methods before/after the pull request changes), NMT can automatically replicate the changes implemented by developers during pull requests in up to 36% of the cases.
Learning How to Mutate Source Code from Bug-Fixes
Starting from code fixed by developers in the context of a bug-fix, our empirical evaluation showed that our models are able to predict mutants that resemble original fixed bugs in between 9% and 45% of the cases (depending on the model).
Software Engineering
SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair
This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning.
Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps
It is common practice for developers of user-facing software to transform a mock-up of a graphical user interface (GUI) into code.
Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities
We aim to reason about the repair ingredients by using code similarities to prioritize and transform statements in a codebase for patch generation.
Software Engineering
