Generative ai for software metadata: Overview of the information retrieval in software engineering track at fire 2023
The Information Retrieval in Software Engineering (IRSE) track aims to develop solutions for
automated evaluation of code comments in a machine learning framework based on human …
automated evaluation of code comments in a machine learning framework based on human …
Automated evaluation of comments to aid software maintenance
S Majumdar, A Bansal, PP Das… - Journal of Software …, 2022 - Wiley Online Library
Approaches to evaluate comments based on whether they increase code comprehensibility
for software maintenance tasks are important, but largely missing. We propose Comment P …
for software maintenance tasks are important, but largely missing. We propose Comment P …
Efficiency of Large Language Models to scale up Ground Truth: Overview of the IRSE Track at Forum for Information Retrieval 2023
The Software Engineering Information Retrieval (IRSE) track aims to devise solutions for the
automated evaluation of code comments within a machine learning framework, with labels …
automated evaluation of code comments within a machine learning framework, with labels …
SoCCMiner: a source code-comments and comment-context miner
Numerous tools exist for mining source code and software development process metrics.
However, very few publicly available tools focus on source code comments, a crucial …
However, very few publicly available tools focus on source code comments, a crucial …
Identification of the relevance of comments in codes using bag of words and transformer based models
T Basu - arxiv preprint arxiv:2308.06144, 2023 - arxiv.org
The Forum for Information Retrieval (FIRE) started a shared task this year for classification of
comments of different code segments. This is binary text classification task where the …
comments of different code segments. This is binary text classification task where the …
A ML-LLM pairing for better code comment classification
H Abi Akl - FIRE (Forum for Information Retrieval Evaluation) 2023, 2023 - inria.hal.science
The" Information Retrieval in Software Engineering (IRSE)" at FIRE 2023 shared task
introduces code comment classification, a challenging task that pairs a code snippet with a …
introduces code comment classification, a challenging task that pairs a code snippet with a …
Enhancing Code Annotation Reliability: Generative AI's Role in Comment Quality Assessment Models
S Killivalavan, D Thenmozhi - arxiv preprint arxiv:2410.22323, 2024 - arxiv.org
This paper explores a novel method for enhancing binary classification models that assess
code comment quality, leveraging Generative Artificial Intelligence to elevate model …
code comment quality, leveraging Generative Artificial Intelligence to elevate model …
Smart Knowledge Transfer using Google-like Search
S Majumdar, PP Das - arxiv preprint arxiv:2308.06653, 2023 - arxiv.org
To address the issue of rising software maintenance cost due to program comprehension
challenges, we propose SMARTKT (Smart Knowledge Transfer), a search framework, which …
challenges, we propose SMARTKT (Smart Knowledge Transfer), a search framework, which …
CSDA: A novel attention-based LSTM approach for code search
L Ren, S Shan, K Wang, K Xue - Journal of Physics: Conference …, 2020 - iopscience.iop.org
Previous studies have proposed semantic-based approaches for code search over large-
scale codebases, which has bridged the gap in understanding the semantics between …
scale codebases, which has bridged the gap in understanding the semantics between …
A ML-LLM pairing for better code comment classification
HA Akl - arxiv preprint arxiv:2310.10275, 2023 - arxiv.org
The" Information Retrieval in Software Engineering (IRSE)" at FIRE 2023 shared task
introduces code comment classification, a challenging task that pairs a code snippet with a …
introduces code comment classification, a challenging task that pairs a code snippet with a …