Codebert For Code Clone Detection: A Replication Study,
2022
Singapore Management University
Codebert For Code Clone Detection: A Replication Study, Saad Arshad, Shamsa Abid, Shafay Shamail
Research Collection School Of Computing and Information Systems
Large pre-trained models have dramatically improved the state-of-the-art on a variety of natural language processing (NLP) tasks. CodeBERT is one such pre-trained model for natural language (NL) and programming language (PL) which captures the semantics in natural language and programming language, and produces general-purpose representations. While it has been shown to support natural language code search and code documentation generation tasks, its effectiveness for code clone detection is not explored in depth. In this paper, we aim to replicate and evaluate the performance of CodeBERT for code clone detection on multiple datasets with varying functionalities to understand (1) whether CodeBERT …
Adding Context To Source Code Representations For Deep Learning,
2022
Singapore Management University
Adding Context To Source Code Representations For Deep Learning, Fuwei Tian, Christoph Treude
Research Collection School Of Computing and Information Systems
Deep learning models have been successfully applied to a variety of software engineering tasks, such as code classification, summarisation, and bug and vulnerability detection. In order to apply deep learning to these tasks, source code needs to be represented in a format that is suitable for input into the deep learning model. Most approaches to representing source code, such as tokens, abstract syntax trees (ASTs), data flow graphs (DFGs), and control flow graphs (CFGs) only focus on the code itself and do not take into account additional context that could be useful for deep learning models. In this paper, we …
Answer Summarization For Technical Queries: Benchmark And New Approach,
2022
Singapore Management University
Answer Summarization For Technical Queries: Benchmark And New Approach, Chengran Yang, Bowen Xu, Ferdian Thung, Yucen Shi, Ting Zhang, Zhou Yang, Xin Zhou, Jieke Shi, Junda He, Donggyun Han, David Lo
Research Collection School Of Computing and Information Systems
Prior studies have demonstrated that approaches to generate an answer summary for a given technical query in Software Question and Answer (SQA) sites are desired. We find that existing approaches are assessed solely through user studies. Hence, a new user study needs to be performed every time a new approach is introduced; this is time-consuming, slows down the development of the new approach, and results from different user studies may not be comparable to each other. There is a need for a benchmark with ground truth summaries as a complement assessment through user studies. Unfortunately, such a benchmark is non-existent …
A Comparative Analysis Of Clone Detection Techniques On Semanticclonebench,
2022
Singapore Management University
A Comparative Analysis Of Clone Detection Techniques On Semanticclonebench, Sohaib Masood Rabbani, Nabeel Ahmad Gulzar, Saad Arshad, Shamsa Abid, Shafay Shamail
Research Collection School Of Computing and Information Systems
Semantic code clone detection involves the detection of functionally similar code fragments which may otherwise be lexically, syntactically, or structurally dissimilar. The detection of semantic code clones has important applications in aspect mining and product line analysis. The accurate detection of semantic code clones is a challenging task and various techniques have been proposed. However, the evaluation of these techniques is performed using various datasets and we do not have a clear picture of the performance of these techniques relative to each other. Recently, SemanticCloneBench has been introduced as a benchmark for semantic clones. Now, we can use the SemanticCloneBench …
Mando: Multi-Level Heterogeneous Graph Embeddings For Fine-Grained Detection Of Smart Contract Vulnerabilities,
2022
Singapore Management University
Mando: Multi-Level Heterogeneous Graph Embeddings For Fine-Grained Detection Of Smart Contract Vulnerabilities, Huu Hoang Nguyen, Nhat Minh Nguyen, Chunyao Xie, Zahra Ahmadi, Daniel Kudenko, Thanh Nam Doan, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Learning heterogeneous graphs consisting of different types of nodes and edges enhances the results of homogeneous graph techniques. An interesting example of such graphs is control-flow graphs representing possible software code execution flows. As such graphs represent more semantic information of code, developing techniques and tools for such graphs can be highly beneficial for detecting vulnerabilities in software for its reliability. However, existing heterogeneous graph techniques are still insufficient in handling complex graphs where the number of different types of nodes and edges is large and variable. This paper concentrates on the Ethereum smart contracts as a sample of software …
Automatic Pull Request Title Generation,
2022
Singapore Management University
Automatic Pull Request Title Generation, Ting Zhang, Ivana Clairine Irsan, Ferdian Thung, Donggyun Han, David Lo, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Pull Requests (PRs) are a mechanism on modern collaborative coding platforms, such as GitHub. PRs allow developers to tell others that their code changes are available for merging into another branch in a repository. A PR needs to be reviewed and approved by the core team of the repository before the changes are merged into the branch. Usually, reviewers need to identify a PR that is in line with their interests before providing a review. By default, PRs are arranged in a list view that shows the titles of PRs. Therefore, it is desirable to have a precise and concise …
Compressing Pre-Trained Models Of Code Into 3 Mb,
2022
Singapore Management University
Compressing Pre-Trained Models Of Code Into 3 Mb, Jieke Shi, Zhou Yang, Bowen Xu, Hong Jin Kang, David Lo
Research Collection School Of Computing and Information Systems
Although large pre-trained models of code have delivered significant advancements in various code processing tasks, there is an impediment to the wide and fluent adoption of these powerful models in software developers’ daily workflow: these large models consume hundreds of megabytes of memory and run slowly on personal devices, which causes problems in model deployment and greatly degrades the user experience. It motivates us to propose Compressor, a novel approach that can compress the pre-trained models of code into extremely small models with negligible performance sacrifice. Our proposed method formulates the design of tiny models as simplifying the pre-trained model …
Lawbreaker: An Approach For Specifying Traffic Laws And Fuzzing Autonomous Vehicles,
2022
Singapore Management University
Lawbreaker: An Approach For Specifying Traffic Laws And Fuzzing Autonomous Vehicles, Yang Sun, Christopher M. Poskitt, Jun Sun, Yuqi Chen, Zijiang Yang
Research Collection School Of Computing and Information Systems
Autonomous driving systems (ADSs) must be tested thoroughly before they can be deployed in autonomous vehicles. High-fidelity simulators allow them to be tested against diverse scenarios, including those that are difficult to recreate in real-world testing grounds. While previous approaches have shown that test cases can be generated automatically, they tend to focus on weak oracles (e.g. reaching the destination without collisions) without assessing whether the journey itself was undertaken safely and satisfied the law. In this work, we propose LawBreaker, an automated framework for testing ADSs against real-world traffic laws, which is designed to be compatible with different scenario …
Guaranteeing Timed Opacity Using Parametric Timed Model Checking,
2022
Singapore Management University
Guaranteeing Timed Opacity Using Parametric Timed Model Checking, Étienne André, Didier Lime, Dylan Marinho, Jun Sun
Research Collection School Of Computing and Information Systems
Information leakage can have dramatic consequences on systems security. Among harmful information leaks, the timing information leakage occurs whenever an attacker successfully deduces confidential internal information. In this work, we consider that the attacker has access (only) to the system execution time. We address the following timed opacity problem: given a timed system, a private location and a final location, synthesize the execution times from the initial location to the final location for which one cannot deduce whether the system went through the private location. We also consider the full timed opacity problem, asking whether the system is opaque for …
Using Natural Language Processing To Increase Modularity And Interpretability Of Automated Essay Evaluation And Student Feedback,
2022
Southern Methodist University
Using Natural Language Processing To Increase Modularity And Interpretability Of Automated Essay Evaluation And Student Feedback, Chris Roche, Nathan Deinlein, Darryl Dawkins, Faizan Javed
SMU Data Science Review
For English teachers and students who are dissatisfied with the one-size-fits-all approach of current Automated Essay Scoring (AES) systems, this research uses Natural Language Processing (NLP) techniques that provide a focus on configurability and interpretability. Unlike traditional AES models which are designed to provide an overall score based on pre-trained criteria, this tool allows teachers to tailor feedback based upon specific focus areas. The tool implements a user-interface that serves as a customizable rubric. Students’ essays are inputted into the tool either by the student or by the teacher via the application’s user-interface. Based on the rubric settings, the tool …
Learning Experience With Learnwithemma,
2022
Multimedia University, Malaysia
Learning Experience With Learnwithemma, Clara Susaie, Choo-Kim Tan, Pey-Yun Goh
Journal of Informatics and Web Engineering
The presence of Covid-19 was a game-changer in all the sectors that traditional learning, working, selling even living methods have changed from basic methods to something else to curb Covid-19. The impact was huge on most sectors due to the lack of experience in overcoming pandemics. One of the sectors that face the most struggle was educational institutions. For many years, face-to-face learning and teaching method have been in practice. While during Covid-19, everyone was forced to attend online classes as a precautious measure. Online learning is completely based on digital study without the physical presence of students or lecturers. …
Secure File Storage On Cloud Using Hybrid Cryptography,
2022
Multimedia University, Malaysia
Secure File Storage On Cloud Using Hybrid Cryptography, Jian-Foo Lai, Swee-Huay Heng
Journal of Informatics and Web Engineering
As technology today is moving forward exponentially, data exchange over the Internet has become a daily routine. Furthermore, businesses are growing internationally and offices are being established in a variety of different places throughout the world. This has resulted in the necessity to make data accessible and practical from any place. As a result, information sent via an may lead to critical security problems involving the breach of secrecy, authentication, and data integrity. This paper introduces a cloud storage system by utilising hybrid cryptography approach that leverages both advantages of symmetric key and asymmetric key cryptographic techniques. In our proposed …
A Systematic Review On Non-Functional Requirements Documentation In Agile Methodology,
2022
Multimedia University, Malaysia
A Systematic Review On Non-Functional Requirements Documentation In Agile Methodology, Steven Loh Mun Keong, Zarina Che Embi
Journal of Informatics and Web Engineering
This systematic literature review studies and summarizes findings of requirements documentation of non-functional requirements practiced by agile software development teams. It identifies current practices and existing gaps when agile software teams discuss and implement non-functional requirements, and the current methods used to document non-functional requirements. Our aim is to identify available evidence on current practices and gaps in documenting non-functional requirements. The review was conducted by searching major databases for publications between 2018 and 2022. The inclusion and exclusion criteria as well as quality assessment scoring criteria were subsequently established. Results show that common themes in the practices and gaps …
Emotion Recognition By Facial Expression And Voice: Review And Analysis,
2022
Multimedia University, Malaysia
Emotion Recognition By Facial Expression And Voice: Review And Analysis, Yixen Lim Lim, Kok-Why Ng, Palanichamy Naveen, Su-Cheng Haw
Journal of Informatics and Web Engineering
Emotion is a scorching topic in the recent years due to the critical unseen stress incurred during the pandemic and post-pandemic. This is worsening with the recent economy’s inflation and increase of living cost, many employees are seriously affected and drawn forth many families saddened cases and tremendous drop of working performance. The increasing stress brings a lot of harm not only to the individual but to the company’s and country’s growth. To recognize emotion through a single model is less accurate, however, recruiting multiple-models may lead to latency in data processing and possibly misleading results if the input models …
Automatic Fairness Testing Of Neural Classifiers Through Adversarial Sampling,
2022
Singapore Management University
Automatic Fairness Testing Of Neural Classifiers Through Adversarial Sampling, Peixin Zhang, Jingyi Wang, Jun Sun, Xinyu Wang, Guoliang Dong, Xinggen Wang, Ting Dai, Jinsong Dong
Research Collection School Of Computing and Information Systems
Although deep learning has demonstrated astonishing performance in many applications, there are still concerns about its dependability. One desirable property of deep learning applications with societal impact is fairness (i.e., non-discrimination). Unfortunately, discrimination might be intrinsically embedded into the models due to the discrimination in the training data. As a countermeasure, fairness testing systemically identifies discriminatory samples, which can be used to retrain the model and improve the model’s fairness. Existing fairness testing approaches however have two major limitations. Firstly, they only work well on traditional machine learning models and have poor performance (e.g., effectiveness and efficiency) on deep learning …
Hierarchical Semantic-Aware Neural Code Representation,
2022
Singapore Management University
Hierarchical Semantic-Aware Neural Code Representation, Yuan Jiang, Xiaohong Su, Christoph Treude, Tiantian Wang
Research Collection School Of Computing and Information Systems
Code representation is a fundamental problem in many software engineering tasks. Despite the effort made by many researchers, it is still hard for existing methods to fully extract syntactic, structural and sequential features of source code, which form the hierarchical semantics of the program and are necessary to achieve a deeper code understanding. To alleviate this difficulty, we propose a new supervised approach based on the novel use of Tree-LSTM to incorporate the sequential and the global semantic features of programs explicitly into the representation model. Unlike previous techniques, our proposed model can not only learn low-level syntactic information within …
Gpgpu Microbenchmarking For Irregular Application Optimization,
2022
Mississippi State University
Gpgpu Microbenchmarking For Irregular Application Optimization, Dalton R. Winans-Pruitt
Theses and Dissertations
Irregular applications, such as unstructured mesh operations, do not easily map onto the typical GPU programming paradigms endorsed by GPU manufacturers, which mostly focus on maximizing concurrency for latency hiding. In this work, we show how alternative techniques focused on latency amortization can be used to control overall latency while requiring less concurrency. We used a custom-built microbenchmarking framework to test several GPU kernels and show how the GPU behaves under relevant workloads. We demonstrate that coalescing is not required for efficacious performance; an uncoalesced access pattern can achieve high bandwidth - even over 80% of the theoretical global memory …
A Tool-Supported Metamodel For Program Bugfix Analysis In Empirical Software Engineering,
2022
CUNY Hunter College
A Tool-Supported Metamodel For Program Bugfix Analysis In Empirical Software Engineering, Manal Zneit
Theses and Dissertations
This thesis describes a software modeling approach aimed at addressing empirical studies in software engineering. We build a metamodel that provides an overview of the taxonomy of program bugfixes in deep learning programs. For modeling purposes, we present a prototype tool that is an implementation of the model-driven techniques presented.
Perturbation Modeling For Molecular Design Of Protein Tyrosine Kinase Inhibitors Using Unsupervised Machine Learning,
2022
Chapman University
Perturbation Modeling For Molecular Design Of Protein Tyrosine Kinase Inhibitors Using Unsupervised Machine Learning, Keerthi Krishnan
Computational and Data Sciences (MS) Theses
The field of computational drug discovery and development has grown, with the aid of new computational tools for novel molecule discovery. In specific, generative deep learning models have excelled as tools to aid in navigating the large space of known molecules and in the creation of new molecules. These models are fed various representations of molecules as inputs and learn to perform a variety of things, such as the optimization of these molecules towards a targeted property. Ultimately, these generative learning models allow us to build bridges between chemical and continuous spaces to understand the compromise between invoking small incremental …
Technology Agency On Usage: Grounded Theory And Measurement Of Technology Induced Usage Behavior,
2022
University of Arkansas, Fayetteville
Technology Agency On Usage: Grounded Theory And Measurement Of Technology Induced Usage Behavior, Sandip Kumar Sarkar
Graduate Theses and Dissertations
Most of today’s software applications involve a dyadic interplay between human and technology agency. The use of algorithms driven by user can alter users' interaction patterns by affording them novel and relevant technology action possibilities. I argue that algorithmic activities and features embedded in apps can keep users on IT applications (apps) for longer periods of time. I refer to the behavior of interacting with the apps for longer time than planned as technology-induced excessive use. While practitioners are beginning to recognize characteristics of technology-induced excessive use, research on this topic is very limited. I used a multimethod approach to …
