Sri Sivasubramaniya Nadar College of Engineering（斯里·西瓦苏布拉马尼亚·纳达尔工程学院）
Samah Syed, Angel Deborah S
备注：
9 pages, 2 figures, 3 tables, Has been accepted for the Information Retrieval in Software Engineering track at Forum for Information Retrieval Evaluation 2023
发表信息：
Information Retrieval in Software Engineering track at Forum for Information Retrieval Evaluation 2023
In software development, code comments play a crucial role in enhancing code comprehension and collaboration. This research paper addresses the challenge of objectively classifying code comments as "Useful" or "Not Useful." We propose a novel solution that harnesses contextualized embeddings, particularly BERT, to automate this classification process. We address this task by incorporating generated code and comment pairs. The initial dataset comprised 9048 pairs of code and comments written in C, labeled as either Useful or Not Useful. To augment this dataset, we sourced an additional 739 lines of code-comment pairs and generated labels using a Large Language Model Architecture, specifically BERT. The primary objective was to build classification models that can effectively differentiate between useful and not useful code comments. Various machine learning algorithms were employed, including Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gradient Boosting, Random Forest, and a Neural Network. Each algorithm was evaluated using precision, recall, and F1-score metrics, both with the original seed dataset and the augmented dataset. This study showcases the potential of generative AI for enhancing binary code comment quality classification models, providing valuable insights for software developers and researchers in the field of natural language processing and software engineering.
A study of the impact of generative AI-based data augmentation on software metadata classification
Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（RUDN大学））；Joint Institute for Nuclear Research（联合核研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Skövde（舍夫德大学）；Indian Institute of Technology (ISM) Dhanbad（印度理工学院（印度矿业学院）丹巴德分校）
Software Metadata Classification based on Generative Artificial Intelligence
Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（卢蒙巴大学））；Joint Institute for Nuclear Research（联合核研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Skövde（舍夫德大学）；Sri Sivasubramaniya Nadar College of Engineering（斯里·西瓦苏布拉马尼亚·纳达尔工程学院）
FMI, Sofia University “St. Kliment Ohridski”（索非亚大学“圣克利门特·奥赫里德斯基”数学与信息学院）；ALT Research Group, Qatar Computing Research Institute, HBKU（卡塔尔计算研究所ALT研究组（哈马德·本·哈利法大学））；IICT, Bulgarian Academy of Sciences（保加利亚科学院信息与通信技术研究所）
We present the system we built for participating in SemEval-2016 Task 3 on Community Question Answering. We achieved the best results on subtask C, and strong results on subtasks A and B, by combining a rich set of various types of features: semantic, lexical, metadata, and user-related. The most important group turned out to be the metadata for the question and for the comment, semantic vectors trained on QatarLiving data and similarities between the question and the comment for subtasks A and C, and between the original and the related question for Subtask B.
Birla Institute of Technology and Science, Pilani（皮拉尼贝拉理工与科学学院）
Online social networks are ubiquitous and user-friendly. Nevertheless, it is vital to detect and moderate offensive content to maintain decency and empathy. However, mining social media texts is a complex task since users don't adhere to any fixed patterns. Comments can be written in any combination of languages and many of them may be low-resource. In this paper, we present our system for the LT-EDI shared task on detecting homophobia and transphobia in social media comments. We experiment with a number of monolingual and multilingual transformer based models such as mBERT along with a data a…
IQLS: Framework for leveraging Metadata to enable Large Language Model based queries to complex, versatile Data
RWTH Aachen University（亚琛工业大学）；Faculty of Engineering and Applied Science, Ontario Tech University（安大略理工大学工程与应用科学学院）；National Research Council Canada（加拿大国家研究委员会）
Schemora: schema matching via multi-stage recommendation and metadata enrichment using off-the-shelf llms
Principal Machine Learning Engineer Informatica（Informatica）；Computer Science University of Wisconsin-Madison（威斯康星大学麦迪逊分校计算机科学系）；Director of AI/ML Development Informatica（Informatica）
AMAQA: A Metadata-based QA Dataset for RAG Systems
Institute for Informatics and Telematics, National Research Council（信息与电信研究院，国家研究院）；Department of Information Engineering, University of Pisa（信息工程系，比萨大学）；Department of Computer Science, University of Pisa（计算机科学系，比萨大学）
DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Kuaishou Technology（快手科技）；Sinosoft Company Limited（中软公司）
Attractive Metadata Attack: Inducing LLM Agents to Invoke Malicious Tools
Cyberspace Institute of Advanced Technology, Guangzhou University（广州大学网络空间研究院）；Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University（香港理工大学电子与电气工程系）
Large Language Models are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence Tasks
College of Computer, National University of Defense Technology（计算机学院，国防科技大学）；Sun Yat-sen University（中山大学）；College of Banking and Financial Studies（金融学院）
A search engine's ability to retrieve desirable datasets is important for data sharing and reuse. Existing dataset search engines typically rely on matching queries to dataset descriptions. However, a user may not have enough prior knowledge to write a query using terms that match with description text.We propose a novel schema label generation model which generates possible schema labels based on dataset table content. We incorporate the generated schema labels into a mixed ranking model which not only considers the relevance between the query and dataset metadata but also the similarity betw…
The Moltbook Observatory Archive: an incremental dataset of agent-only social network activity
Simula Metropolitan Center for Digital Engineering (SimulaMet)（Simula数字工程中心（SimulaMet））；Oslo Metropolitan University（奥斯陆大学）；Simula Research Laboratory（Simula研究实验室）
A Standardized Machine-readable Dataset Documentation Format for Responsible AI
Universitat Oberta de Catalunya（加泰罗尼亚开放大学）；NASA IMPACT（美国国家航空航天局IMPACT）；Google（谷歌）；OpenML；Bayer（拜耳）；Dotphoton；NASA（美国国家航空航天局）；Meta；Oak Ridge National Laboratory（橡树岭国家实验室）；Plaixus Ltd（Plaixus有限公司）；North Carolina State University（北卡罗来纳州立大学）；Graphcore
Leveraging Weakly Annotated Data for Hate Speech Detection in Code-Mixed Hinglish: A Feasibility-Driven Transfer Learning Approach with Large Language Models
Istituto per le Applicazioni del Calcolo Mauro Picone, Italian National Research Council（意大利国家研究委员会 Mauro Picone 应用计算研究所）；Università Campus Bio-Medico di Roma（罗马校园生物医学大学）；UniCamillus-Saint Camillus International University of Health Sciences（圣卡米勒斯国际健康科学大学）
Links are an essential feature of the World Wide Web, and source code repositories are no exception. However, despite their many undisputed benefits, links can suffer from decay, insufficient versioning, and lack of bidirectional traceability. In this paper, we investigate the role of links contained in source code comments from these perspectives. We conducted a large-scale study of around 9.6 million links to establish their prevalence, and we used a mixed-methods approach to identify the links' targets, purposes, decay, and evolutionary aspects. We found that links are prevalent in source c…
Understanding binary code is an essential but complex software engineering task for reverse engineering, malware analysis, and compiler optimization. Unlike source code, binary code has limited semantic information, which makes it challenging for human comprehension. At the same time, compiling source to binary code, or transpiling among different programming languages (PLs) can provide a way to introduce external knowledge into binary comprehension. We propose to develop Artificial Intelligence (AI) models that aid human comprehension of binary code. Specifically, we propose to incorporate do…
TODO comments are very widely used by software developers to describe their pending tasks during software development. However, after performing the task developers sometimes neglect or simply forget to remove the TODO comment, resulting in obsolete TODO comments. These obsolete TODO comments can confuse development teams and may cause the introduction of bugs in the future, decreasing the software's quality and maintainability. In this work, we propose a novel model, named TDCleaner (TODO comment Cleaner), to identify obsolete TODO comments in software projects. TDCleaner can assist developer…
Code review is one of the best practices as a powerful safeguard for software quality. In practice, senior or highly skilled reviewers inspect source code and provide constructive comments, considering what authors may ignore, for example, some special cases. The collaborative validation between contributors results in code being highly qualified and less chance of bugs. However, since personal knowledge is limited and varies, the efficiency and effectiveness of code review practice are worthy of further improvement. In fact, it still takes a colossal and time-consuming effort to deliver usefu…
Self-admitted technical debt (SATD) refers to technical debt that is intentionally introduced by developers and explicitly documented in code comments or other software artifacts (e.g., issue reports) to annotate sub-optimal decisions made by developers in the software development process. In this work, we take the first look at the existence and characteristics of duplicate and near-duplicate SATD comments in five popular Apache OSS projects, i.e., JSPWiki, Helix, Jackrabbit, Archiva, and SystemML. We design a method to automatically identify groups of duplicate and near-duplicate SATD commen…
A large-scale dataset of Android applications and their SDK dependencies
IMT School for Advanced Studies（高等研究学院）；Istituto Nazionale di Alta Matematica ‘Francesco Severi’ (INdAM-GNAMPA)（高等数学国家研究所‘弗朗切斯科·塞韦里’）；IUSS University School for Advanced Studies（高级研究大学学院）
