Software Engineering Commons^™

A Comparison Of Opennmt Sequence Model For Indonesian Automatic Question Generation, Yuniar Indrihapsari, Handaru Jati, N. Nurkhamid, Ratna Wardani, Pradana Setialana, Muhamad Izzudin Mahali, Danang Wijaya, Dhista Dwi Nur Ardiansyah, Satya Adhiyaksa Ardy, Maria Bernadetha Charlotta Wonda Tiala, Andi Hakim Al-Khawarizmi, Widya Ardiyanto

Elinvo (Electronics, Informatics, and Vocational Education)

Evaluation of learners is a crucial aspect of the educational system. However, creating evaluation instruments is a process that demands teachers' time and energy. The researcher developed the Indonesia Automatic Question Generator in this study using an architecture modified from past studies. The primary goals of this project are (1) to construct an AQG tool utilizing the OpenNMT series and (2) to analyze and compare the model's performance. As a data source, this study employs the SQuAD 2.0 dataset and numerous sequence techniques, including BiGRU, BiLSTM, and Transformer. The researcher trained the models using OpenNMT-py and Google Collaboratory. This approach …

Development Of An Android-Based Cultural Heritage Map App, Muhammad Irfan Luthfi, Ratna Wardani, Nisa Dwi Septiyanti

Elinvo (Electronics, Informatics, and Vocational Education)

This study aims to (1) develop an Android-based cultural heritage map app that can provide the location of cultural heritages in the Special Region in Yogyakarta along with its descriptions, images, and videos; and (2) understand the quality of the app based on standard software quality testing ISO / IEC 25010 on functional suitability, performance efficiency, and compatibility. The development method utilized a waterfall software development model that consists of communication, planning, modeling, construction, and deployment. The results of this study are 1) an Android-based cultural heritage map app that can provide the location of cultural heritages in the Special …

Cross-Language Tourism News Retrieval System Using Google Translate Api On Sebi Search Engine, H. Husni, Arif Muntasa, Sigit Susanto Putro, Zulfi Osman

Elinvo (Electronics, Informatics, and Vocational Education)

Cross-Language Information Retrieval (CLIR) is responsible for retrieving information stored in a language different from the language of the query provided by the user. Some translation methods commonly used in CLIR are Dictionary, Parallel corpora, Comparable corpora, Machine translator, Ontology, and Transitive-based. The query must be translated to the target language, followed by preprocessing and calculating the similarity between the query and all documents in the corpus. The problem is the time and accuracy of query translation. Moreover, the queries are not written as complete sentences according to certain language rules. Stemming, for example, every language has its own method. …

Machine Learning System Implementation Of Education Podcast Recommendations On Spotify Applications Using Content-Based Filtering And Tf-Idf, Muhammad Mukti Raharjo, Fatchul Arifin

Elinvo (Electronics, Informatics, and Vocational Education)

Spotify, this popular music and podcast streaming service, has a fundamental problem in assisting clients in finding podcasts that fit their interests. Thus, the goal of this project is to develop a podcast recommendation system that would enhance users' capacity to identify pertinent content, particularly in the educational genre. By using content-based filtration techniques, this system analyzes the user's listening preferences and interests before recommending educational podcasts. The podcast data source is Spotify, and the suggestions are produced using the TF-IDF and Cosine Similarity techniques. The recommendations provide a list of educational podcasts catered to the user's specific interests. The …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

A Critical Computing Curriculum Design Case: Exploring Tribal Sovereignty For Middle School Students, Kristin A. Searle, Aubrey Rogowski, Colby Tofel-Grehl, Mengying Jiang

Journal of Computer Science Integration

We report on our efforts to design an integrated computing curriculum for middle school students in Montana that is in line with the Kapor Center’s focus on culturally sustaining-revitalizing pedagogies. Montana provides a unique context for doing this work because a state constitutional mandate requires all K-12 students to learn about tribal histories and cultures through Indian Education For All (IEFA). IEFA centers around seven essential understandings about Indigenous peoples in Montana that are integrated across content areas. In addition, implementation of Montana’s CS standards began in the 2021–2022 school year. In the curricular design, we sought to bring together …

Employing An Abolitionist, Critical Race Pedagogy In Cs: Centering The Voices, Experiences And Technological Innovations Of Black Youth, Tiera Tanksley

Journal of Computer Science Integration

This paper proposes a pedagogical extension of culturally responsive praxis called abolitionist, critical race pedagogy in CS. To showcase the power and potentiality of this pedagogy, this paper examines the experiences of 2 cohorts of Black high school students (n = 30) who participated in a critical race technology course that was taught during the dual pandemic of COVID-19 and anti-Black racism. The goal of this summer course was to employ an abolitionist, critical race pedagogy in CS to foster Black students’ ability to critically examine the ubiquity of anti-Black racism within the socio-technical architectures (e.g. code, data, algorithms and …

Culturally Responsive-Sustaining Computational Thinking: Enactment In Elementary Classrooms, Victoria Macann, Aman Yadav

Journal of Computer Science Integration

Technology has increasingly permeated many aspects of everyday life and this evolution raises the need for individuals to understand how the digital world works and what opportunities and risks it brings (Nouri, Zhang, Mannila & Norén, 2019). For this to be an experience for everyone, we need to rethink how we integrate computational thinking (CT) and provide teachers with tools to center their students’ identities, experiences, and cultures in the classroom. In this paper, we present two case studies of primary (elementary) teachers from a full primary (student ages 5–13) semi-rural school in the North Island of New Zealand that …

Cm-Ii Meditation As An Intervention To Reduce Stress And Improve Attention: A Study Of Ml Detection, Spectral Analysis, And Hrv Metrics, Sreekanth Gopi

Master of Science in Computer Science Theses

Students frequently face heightened stress due to academic and social pressures, particularly in de- manding fields like computer science and engineering. These challenges are often associated with serious mental health issues, including ADHD (Attention Deficit Hyperactivity Disorder), depression, and an increased risk of suicide. The average student attention span has notably decreased from 21⁄2 minutes to just 47 seconds, and now it typically takes about 25 minutes to switch attention to a new task (Mark, 2023). Research findings suggest that over 95% of individuals who die by suicide have been diagnosed with depression (Shahtahmasebi, 2013), and almost 20% of students …

Μakka: Mutation Testing For Actor Concurrency In Akka Using Real-World Bugs, Mohsen Moradi Moghadam, Mehdi Bagherzadeh, Raffi Khatchadourian, Hamid Bagheri

Publications and Research

Actor concurrency is becoming increasingly important in the real-world and mission-critical software. This requires these applications to be free from actor bugs, that occur in the real world, and have tests that are effective in finding these bugs. Mutation testing is a well-established technique that transforms an application to induce its likely bugs and evaluate the effectiveness of its tests in finding these bugs. Mutation testing is available for a broad spectrum of applications and their bugs, ranging from web to mobile to machine learning, and is used at scale in companies like Google and Facebook. However, there still is …

Adaptable Object And Animation System For Game Development, Isaiah Turner

Masters Theses & Specialist Projects

In contemporary times, video games have swiftly evolved into a prominent medium, excelling in both entertainment and narrative delivery, positioning themselves as significant rivals to traditional forms such as film and theater. The burgeoning popularity of gaming has led to a surge in aspiring game developers seeking to craft their own creations, driven by both commercial aspirations and personal passion. However, a common challenge faced by these individuals involves the considerable time investment required to acquire essential skills and establish a foundational framework for their projects. Accessible game development engines that offer a diverse range of fundamental features play a …

Vertical Free-Swinging Photovoltaic Racking Energy Modeling: A Novel Approach To Agrivoltaics, Koami Soulemane Hayibo, Joshua M. Pearce

Electrical and Computer Engineering Publications

To enable lower-cost building materials, a free-swinging bifacial vertical solar photovoltaic (PV) rack has been proposed, which complies with Canadian building codes and is the lowest capital-cost agrivoltaics rack. The wind force applied to the free-swinging PV, however, causes it to have varying tilt angles depending on the wind speed and direction. No energy performance model accurately describes such a system. To provide a simulation model for the free-swinging PV, where wind speed and direction govern the array tilt angle, this study builds upon the open-source System Advisor Model (SAM) using Python. After the SAM python model is validated, a …

Mrim: Lightweight Saliency-Based Mixed-Resolution Imaging For Low-Power Pervasive Vision, Jiyan Wu, Vithurson Subasharan, Minh Anh Tuan Tran, Kasun Pramuditha Gamlath, Archan Misra

Research Collection School Of Computing and Information Systems

While many pervasive computing applications increasingly utilize real-time context extracted from a vision sensing infrastructure, the high energy overhead of DNN-based vision sensing pipelines remains a challenge for sustainable in-the-wild deployment. One common approach to reducing such energy overheads is the capture and transmission of lower-resolution images to an edge node (where the DNN inferencing task is executed), but this results in an accuracy-vs-energy tradeoff, as the DNN inference accuracy typically degrades with a drop in resolution. In this work, we introduce MRIM, a simple but effective framework to tackle this tradeoff. Under MRIM, the vision sensor platform first executes …

A Reliable And Secure Mobile Cyber-Physical Digital Microfluidic Biochip For Intelligent Healthcare, Yinan Yao, Decheng Qiu, Huangda Liu, Zhongliao Yang, Ximeng Liu, Yang Yang, Chen Dong

Research Collection School Of Computing and Information Systems

Digital microfluidic, as an emerging and potential technology, diversifies the biochemical applications platform, such as protein dilution sewage detection. At present, a vast majority of universal cyberphysical digital microfluidic biochips (DMFBs) transmit data through wires via personal computers and microcontrollers (like Arduino), consequently, susceptible to various security threats and with the popularity of wireless devices, losing competitiveness gradually. On the premise that security be ensured first and foremost, calls for wireless portable, safe, and economical DMFBs are imperative to expand their application fields, engage more users, and cater to the trend of future wireless communication. To this end, a new …

Electronic Note-String Detector, Gavin Garcia-Rossi, Tommy Smail

Electrical Engineering

As the virtual space has become a dominant part of everyone’s day-to-day lives, many normal face-to-face interactions and services have not yet been facilitated by adapting technology. One of these prevailing areas is music lessons. Over Zoom meetings, or other virtual platforms, it is tremendously challenging to teach students. These challenges include recognizing student mistakes audibly and visually, and being able to give confident feedback on the incorrect notes played by learning musicians. Without having to delve into improving the complex systems that would be required to improve audio, video, and connection quality of these connections, we have another solution …

Deeparc: Modularizing Neural Networks For The Model Maintenance, Xiaoning Ren, Yun Lin, Yinxing Xue, Ruofan Liu, Jun Sun, Zhiyong Feng, Jinsong Dong

Research Collection School Of Computing and Information Systems

Neural networks are an emerging data-driven programming paradigm widely used in many areas. Unlike traditional software systems consisting of decomposable modules, a neural network is usually delivered as a monolithic package, raising challenges for some maintenance tasks such as model restructure and re-adaption. In this work, we propose DeepArc, a novel modularization method for neural networks, to reduce the cost of model maintenance tasks. Specifically, DeepArc decomposes a neural network into several consecutive modules, each of which encapsulates consecutive layers with similar semantics. The network modularization facilitates practical tasks such as refactoring the model to preserve existing features (e.g., model …

Memory Network-Based Interpreter Of User Preferences In Content-Aware Recommender Systems, Nhu Thuat Tran, Hady W. Lauw

Research Collection School Of Computing and Information Systems

This article introduces a novel architecture for two objectives recommendation and interpretability in a unified model. We leverage textual content as a source of interpretability in content-aware recommender systems. The goal is to characterize user preferences with a set of human-understandable attributes, each is described by a single word, enabling comprehension of user interests behind item adoptions. This is achieved via a dedicated architecture, which is interpretable by design, involving two components for recommendation and interpretation. In particular, we seek an interpreter, which accepts holistic user’s representation from a recommender to output a set of activated attributes describing user preferences. …

Refinement-Based Specification And Analysis Of Multi-Core Arinc 653 Using Event-B, Feng Zhang, Leping Zhang, Yongwang Zhao, Yang Liu, Jun Sun

Research Collection School Of Computing and Information Systems

ARINC 653 as the de facto standard of partitioning operating systems has been applied in many safety-critical domains. The multi-core version of ARINC 653, ARINC 653 Part 1-4 (Version 4), provides support for services to be utilized with a module that contains multiple processor cores. Formal specification and analysis of this standard document could provide a rigorous specification and uncover concealed errors in the textual description of service requirements. This article proposes a specification method for concurrency on a multi-core platform using Event-B, and a refinement structure for the complicated ARINC 653 Part 1-4 provides a comprehensive, stepwise refinement-based Event-B …

Software Architecture In Practice: Challenges And Opportunities, Zhiyuan Wan, Yun Zhang, Xin Xia, Yi Jiang, David Lo

Research Collection School Of Computing and Information Systems

Software architecture has been an active research field for nearly four decades, in which previous studies make significant progress such as creating methods and techniques and building tools to support software architecture practice. Despite past efforts, we have little understanding of how practitioners perform software architecture related activities, and what challenges they face. Through interviews with 32 practitioners from 21 organizations across three continents, we identified challenges that practitioners face in software architecture practice during software development and maintenance. We reported on common software architecture activities at software requirements, design, construction and testing, and maintenance stages, as well as corresponding …

Evaluating Transfer Learning For Simplifying Github Readmes, Haoyu Gao, Christoph Treude, Mansooreh Zahedi

Research Collection School Of Computing and Information Systems

Software documentation captures detailed knowledge about a software product, e.g., code, technologies, and design. It plays an important role in the coordination of development teams and in conveying ideas to various stakeholders. However, software documentation can be hard to comprehend if it is written with jargon and complicated sentence structure. In this study, we explored the potential of text simplification techniques in the domain of software engineering to automatically simplify GitHub README files. We collected software-related pairs of GitHub README files consisting of 14,588 entries, aligned difficult sentences with their simplified counterparts, and trained a Transformer-based model to automatically simplify …

Lessons From The Long Tail: Analysing Unsafe Dependency Updates Across Software Ecosystems, Supatsara Wattanakriengkrai, Raula Kula, Christoph Treude, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

A risk in adopting third-party dependencies into an application is their potential to serve as a doorway for malicious code to be injected (most often unknowingly). While many initiatives from both industry and research communities focus on the most critical dependencies (i.e., those most depended upon within the ecosystem), little is known about whether the rest of the ecosystem suffers the same fate. Our vision is to promote and establish safer practises throughout the ecosystem. To motivate our vision, in this paper, we present preliminary data based on three representative samples from a population of 88,416 pull requests (PRs) and …

Kape: Knn-Based Performance Testing For Deep Code Search, Yuejun Guo, Qiang Hu, Xiaofei Xie, Cordy Maxime, Mike Papadakis, Yves Le Traon

Research Collection School Of Computing and Information Systems

Code search is a common yet important activity of software developers. An efficient code search model can largely facilitate the development process and improve the programming quality. Given the superb performance of learning the contextual representations, deep learning models, especially pre-trained language models, have been widely explored for the code search task. However, studies mainly focus on proposing new architectures for ever-better performance on designed test sets but ignore the performance on unseen test data where only natural language queries are available. The same problem in other domains, e.g., CV and NLP, is usually solved by test input selection that …

Interpreting Codebert For Semantic Code Clone Detection, Shamsa Abid, Xuemeng Cai, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Accurate detection of semantic code clones has many applications in software engineering but is challenging because of lexical, syntactic, or structural dissimilarities in code. CodeBERT, a popular deep neural network based pre-trained code model, can detect code clones with a high accuracy. However, its performance on unseen data is reported to be lower. A challenge is to interpret CodeBERT's clone detection behavior and isolate the causes of mispredictions. In this paper, we evaluate CodeBERT and interpret its clone detection behavior on the SemanticCloneBench dataset focusing on Java and Python clone pairs. We introduce the use of a black-box model interpretation …

Software Composition Analysis For Vulnerability Detection: An Empirical Study On Java Projects, Lida Zhao, Sen Chen, Zhengzi Xu, Lyuye Zhang, Jiahui Wu, Jun Sun, Yang Liu

Research Collection School Of Computing and Information Systems

Software composition analysis (SCA) tools are proposed to detect potential vulnerabilities introduced by open-source software (OSS) imported as third-party libraries (TPL). With the increasing complexity of software functionality, SCA tools may encounter various scenarios during the dependency resolution process, such as diverse formats of artifacts, diverse dependency imports, and diverse dependency specifications. However, there still lacks a comprehensive evaluation of SCA tools for Java that takes into account the above scenarios. This could lead to a confined interpretation of comparisons, improper use of tools, and hinder further improvements of the tools. To fill this gap, we proposed an Evaluation Model …

Supporting Software Engineers With Large Language Model-Based Automation, Ting Zhang

Dissertations and Theses Collection (Open Access)

In recent years, software engineering (SE) has witnessed significant growth, leading to the creation and sharing of an abundance of software artifacts such as source code, bug reports, and pull requests. Analyzing these artifacts is crucial for comprehending the sentiments of software developers and automating various SE tasks, ultimately leading to more human-centered automated SE and enhancing software development efficiency. However, the diverse and unstructured nature of software text poses a significant challenge to this analysis. In response, researchers have investigated a variety of approaches, including the utilization of natural language processing techniques. The advent of large language models (LLMs), …

Towards Securing Smart Contracts Systematically, Duy Tai Nguyen

Dissertations and Theses Collection (Open Access)

Smart contracts are a groundbreaking technique that allows users to programmatically modify the state of the blockchain. They are essentially self-enforcing programs that are deployed and executed on top of the blockchain. In recent years, we have witnessed various smart contract incidents that led to substantial financial losses and even business closures. These incidents mainly arise from design flaws in Solidity, a dominant programming language for writing smart contracts, which complicates the process of detecting and repairing vulnerabilities. Furthermore, there is a growing interest in attacking smart contracts by the attackers. This thesis is dedicated to developing effective methods to …

How Helpful Do Novice Programmers Find The Feedback Of An Automated Repair Tool?, Oka Kurniawan, Christopher M. Poskitt, Ismam Al Hoque, Norman Tiong Seng Lee, Cyrille Jégourel, Nachamma Sockalingam

Research Collection School Of Computing and Information Systems

Immediate feedback has been shown to improve student learning. In programming courses, immediate, automated feedback is typically provided in the form of pre-defined test cases run by a submission platform. While these are excellent for highlighting the presence of logical errors, they do not provide novice programmers enough scaffolding to help them identify where an error is or how to fix it. To address this, several tools have been developed that provide richer feedback in the form of program repairs. Studies of such tools, however, tend to focus more on whether correct repairs can be generated, rather than how novices …

A Closer Look At The Security Risks In The Rust Ecosystem, Xiaoye Zheng, Zhiyuan Wan, Yun Zhang, Rui Chang, David Lo

Research Collection School Of Computing and Information Systems

Rust is an emerging programming language designed for the development of systems software. To facilitate the reuse of Rust code, crates.io, as a central package registry of the Rust ecosystem, hosts thousands of third-party Rust packages. The openness of crates.io enables the growth of the Rust ecosystem but comes with security risks by severe security advisories. Although Rust guarantees a software program to be safe via programming language features and strict compile-time checking, the unsafe keyword in Rust allows developers to bypass compiler safety checks for certain regions of code. Prior studies empirically investigate the memory safety and concurrency bugs …

Exgen: Ready-To-Use Exercise Generation In Introductory Programming Courses, Nguyen Binh Duong Ta, Hua Gia Phuc Nguyen, Gottipati Swapna

Research Collection School Of Computing and Information Systems

In introductory programming courses, students as novice programmers would benefit from doing frequent practices set at a difficulty level and concept suitable for their skills and knowledge. However, setting many good programming exercises for individual learners is very time-consuming for instructors. In this work, we propose an automated exercise generation system, named ExGen, which leverages recent advances in pre-trained large language models (LLMs) to automatically create customized and ready-to-use programming exercises for individual students ondemand. The system integrates seamlessly with Visual Studio Code, a popular development environment for computing students and software engineers. ExGen effectively does the following: 1) maintaining …

On The Usage Of Continual Learning For Out-Of-Distribution Generalization In Pre-Trained Language Models Of Code, Martin Weyssow, Xin Zhou, Kisub Kim, David Lo, Houari A. Sahraoui

Research Collection School Of Computing and Information Systems

Pre-trained language models (PLMs) have become a prevalent technique in deep learning for code, utilizing a two-stage pre-training and fine-tuning procedure to acquire general knowledge about code and specialize in a variety of downstream tasks. However, the dynamic nature of software codebases poses a challenge to the effectiveness and robustness of PLMs. In particular, world-realistic scenarios potentially lead to significant differences between the distribution of the pre-training and test data, i.e., distribution shift, resulting in a degradation of the PLM's performance on downstream tasks. In this paper, we stress the need for adapting PLMs of code to software data whose …