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The Vehicle Routing Problem With Simultaneous Pickup And Delivery And Occasional Drivers, Vincent F. YU, Grace ALOINA, Panca JODIAWAN, Aldy GUNAWAN, Tsung-C. HUANG 2023 Singapore Management University

The Vehicle Routing Problem With Simultaneous Pickup And Delivery And Occasional Drivers, Vincent F. Yu, Grace Aloina, Panca Jodiawan, Aldy Gunawan, Tsung-C. Huang

Research Collection School Of Computing and Information Systems

This research addresses the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Occasional Drivers (VRPSPDOD), which is inspired from the importance of addressing product returns and the emerging notion of involving available crowds to perform pickup and delivery activities in exchange for some compensation. At the depot, a set of regular vehicles is available to deliver and/or pick up customers’ goods. A set of occasional drivers, each defined by their origin, destination, and flexibility, is also able to help serve the customers. The objective of VRPSPDOD is to minimize the total traveling cost of operating regular vehicles and total …


Improving Developers' Understanding Of Regex Denial Of Service Tools Through Anti-Patterns And Fix Strategies, Sk Adnan Hassan, Zainab Aamir, Dongyoon Lee, James C. Davis, Francisco Servant 2023 Virginia Tech

Improving Developers' Understanding Of Regex Denial Of Service Tools Through Anti-Patterns And Fix Strategies, Sk Adnan Hassan, Zainab Aamir, Dongyoon Lee, James C. Davis, Francisco Servant

Department of Electrical and Computer Engineering Faculty Publications

Regular expressions are used for diverse purposes, including input validation and firewalls. Unfortunately, they can also lead to a security vulnerability called ReDoS (Regular Expression Denial of Service), caused by a super-linear worst-case execution time during regex matching. Due to the severity and prevalence of ReDoS, past work proposed automatic tools to detect and fix regexes. Although these tools were evaluated in automatic experiments, their usability has not yet been studied; usability has not been a focus of prior work. Our insight is that the usability of existing tools to detect and fix regexes will improve if we complement them …


Famaid: A Tool For Aiding People With Disability, Mohamad Maad, Ahmad Owaydate, Mohammad Kojok, Firas Aboudaher, Layal Abu Daher, May Itani 2022 Department of Mathematics and Computer Science, Beirut Arab University

Famaid: A Tool For Aiding People With Disability, Mohamad Maad, Ahmad Owaydate, Mohammad Kojok, Firas Aboudaher, Layal Abu Daher, May Itani

BAU Journal - Science and Technology

People with disabilities suffer from discrimination and obstacles that restrict them from participating in society on an equal basis with others every day. They are deprived of their rights to be included in ordinary school systems and even in the work market. In the process of raising awareness, facilitating dailyroutines, and developing guidance, the idea of assisting such people with handy tools/software arose and was implemented in the FamAid tool. FamAid offers people with hearing disability the opportunity to be engaged in the society through many facilities. In this work, we implemented a web application that serves as a community …


Hybrid Life Cycles In Software Development, Eric Vincent Schoenborn 2022 Grand Valley State University

Hybrid Life Cycles In Software Development, Eric Vincent Schoenborn

Culminating Experience Projects

This project applied software specification gathering, architecture, work planning, and development to a real-world development effort for a local business. This project began with a feasibility meeting with the owner of Zeal Aerial Fitness. After feasibility was assessed the intended users, needed functionality, and expected user restrictions were identified with the stakeholders. A hybrid software lifecycle was selected to allow a focus on base functionality up front followed by an iterative development of expectations of the stakeholders. I was able to create various specification diagrams that express the end projects goals to both developers and non-tech individuals using a standard …


Defining Traffic Scenarios For The Visually Impaired, Judith Jakob, Kordula Kugele, József Tick 2022 Furtwangen University of Applied Sciences

Defining Traffic Scenarios For The Visually Impaired, Judith Jakob, Kordula Kugele, József Tick

The Qualitative Report

For the development of a transfer concept of camera-based object detections from Advanced Driver Assistance Systems to the assistance of the visually impaired, we define relevant traffic scenarios and vision use cases by means of problem-centered interviews with four experts and ten members of the target group. We identify the six traffic scenarios: general orientation, navigating to an address, crossing a road, obstacle avoidance, boarding a bus, and at the train station clustered into the three categories: Orientation, Pedestrian, and Public Transport. Based on the data, we describe each traffic scenario and derive a summarizing table adapted from software engineering …


Payload-Byte: A Tool For Extracting And Labeling Packet Capture Files Of Modern Network Intrusion Detection Datasets, Yasir Farrukh, Irfan Khan, Syed Wali, David A. Bierbrauer, John Pavlik, Nathaniel D. Bastian 2022 Army Cyber Institute, United States Military Academy

Payload-Byte: A Tool For Extracting And Labeling Packet Capture Files Of Modern Network Intrusion Detection Datasets, Yasir Farrukh, Irfan Khan, Syed Wali, David A. Bierbrauer, John Pavlik, Nathaniel D. Bastian

ACI Journal Articles

Adapting modern approaches for network intrusion detection is becoming critical, given the rapid technological advancement and adversarial attack rates. Therefore, packet-based methods utilizing payload data are gaining much popularity due to their effectiveness in detecting certain attacks. However, packet-based approaches suffer from a lack of standardization, resulting in incomparability and reproducibility issues. Unlike flow-based datasets, no standard labeled dataset exists, forcing researchers to follow bespoke labeling pipelines for individual approaches. Without a standardized baseline, proposed approaches cannot be compared and evaluated with each other. One cannot gauge whether the proposed approach is a methodological advancement or is just being benefited …


Software Supply Chain Security Attacks And Analysis Of Defense, Juanjose Rodriguez-Cardenas, Jobair Hossain Faruk, Masura Tansim, Asia Shavers, Corey Brookins, Shamar Lake, Ava Norouzi, Marie Nassif, Kenneth Burke, Miranda Dominguez 2022 Kennesaw State University

Software Supply Chain Security Attacks And Analysis Of Defense, Juanjose Rodriguez-Cardenas, Jobair Hossain Faruk, Masura Tansim, Asia Shavers, Corey Brookins, Shamar Lake, Ava Norouzi, Marie Nassif, Kenneth Burke, Miranda Dominguez

Symposium of Student Scholars

The Software Supply chain or SSC is the backbone of the logistics industry and is crucial to a business's success and operation. The surge of attacks and risks for the SSC has grown in coming years with each attack's impact becoming more significant. These attacks have led to the leaking of both client and company sensitive information, corruption of the data, and having it subject to malware and ransomware installation, despite new practices implemented and investments into SSC security and its branches that have not stopped attackers from developing new vulnerabilities and exploits. In our research, we have investigated Software …


Opportunities And Challenges In Code Search Tools, Chao LIU, Xin XIA, David LO, Cuiying GAO, Xiaohu YANG, John GRUNDY 2022 Zhejiang University

Opportunities And Challenges In Code Search Tools, Chao Liu, Xin Xia, David Lo, Cuiying Gao, Xiaohu Yang, John Grundy

Research Collection School Of Computing and Information Systems

Code search is a core software engineering task. Effective code search tools can help developers substantially improve their software development efficiency and effectiveness. In recent years, many code search studies have leveraged different techniques, such as deep learning and information retrieval approaches, to retrieve expected code from a large-scale codebase. However, there is a lack of a comprehensive comparative summary of existing code search approaches. To understand the research trends in existing code search studies, we systematically reviewed 81 relevant studies. We investigated the publication trends of code search studies, analyzed key components, such as codebase, query, and modeling technique …


Biasfinder: Metamorphic Test Generation To Uncover Bias For Sentiment Analysis Systems, Muhammad Hilmi ASYROFI, Zhou YANG, IMAM NUR BANI YUSUF, Hong Jin KANG, Thung Ferdian, David LO 2022 Singapore Management University

Biasfinder: Metamorphic Test Generation To Uncover Bias For Sentiment Analysis Systems, Muhammad Hilmi Asyrofi, Zhou Yang, Imam Nur Bani Yusuf, Hong Jin Kang, Thung Ferdian, David Lo

Research Collection School Of Computing and Information Systems

Artificial intelligence systems, such as Sentiment Analysis (SA) systems, typically learn from large amounts of data that may reflect human bias. Consequently, such systems may exhibit unintended demographic bias against specific characteristics (e.g., gender, occupation, country-of-origin, etc.). Such bias manifests in an SA system when it predicts different sentiments for similar texts that differ only in the characteristic of individuals described. To automatically uncover bias in SA systems, this paper presents BiasFinder, an approach that can discover biased predictions in SA systems via metamorphic testing. A key feature of BiasFinder is the automatic curation of suitable templates from any given …


Deep Just-In-Time Defect Localization, Fangcheng QIU, Zhipeng GAO, Xin XIA, David LO, John GRUNDY, Xinyu WANG 2022 Singapore Management University

Deep Just-In-Time Defect Localization, Fangcheng Qiu, Zhipeng Gao, Xin Xia, David Lo, John Grundy, Xinyu Wang

Research Collection School Of Computing and Information Systems

During software development and maintenance, defect localization is an essential part of software quality assurance. Even though different techniques have been proposed for defect localization, i.e., information retrieval (IR)-based techniques and spectrum-based techniques, they can only work after the defect has been exposed, which can be too late and costly to adapt to the newly introduced bugs in the daily development. There are also many JIT defect prediction tools that have been proposed to predict the buggy commit. But these tools do not locate the suspicious buggy positions in the buggy commit. To assist developers to detect bugs in time …


Redefining Research In Nanotechnology Simulations: A New Approach To Data Caching And Analysis, Darin Tsai, Alan Zhang, Aloysius Rebeiro 2022 Purdue University

Redefining Research In Nanotechnology Simulations: A New Approach To Data Caching And Analysis, Darin Tsai, Alan Zhang, Aloysius Rebeiro

The Journal of Purdue Undergraduate Research

No abstract provided.


Predictive Self-Organizing Neural Networks For In-Home Detection Of Mild Cognitive Impairment, Seng Khoon TEH, Iris RAWTAER, Ah-hwee TAN 2022 Singapore Management University

Predictive Self-Organizing Neural Networks For In-Home Detection Of Mild Cognitive Impairment, Seng Khoon Teh, Iris Rawtaer, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

In-home sensing of daily living patterns from older adults coupled with machine learning is a promisingapproach to detect Mild Cognitive Impairment (MCI), a potentially reversible condition with early detectionand appropriate intervention. However, the number of subjects involved in such real-world studies istypically limited, posing the so-called small data problem to most predictive models which rely on a sizablenumber of labeled data. In this work, a predictive self-organizing neural network known as fuzzy AdaptiveResonance Associate Map (fuzzy ARAM) is proposed to detect MCI using in-home sensor data collected from aunique Singapore cross-sectional study. Specifically, mean and standard deviation of nine in-home …


Real World Projects, Real Faults: Evaluating Spectrum Based Fault Localization Techniques On Python Projects, RATNADIRA WIDYASARI, Gede Artha Azriadi PRANA, Stefanus AGUS HARYONO, Shaowei WANG, David LO 2022 Singapore Management University

Real World Projects, Real Faults: Evaluating Spectrum Based Fault Localization Techniques On Python Projects, Ratnadira Widyasari, Gede Artha Azriadi Prana, Stefanus Agus Haryono, Shaowei Wang, David Lo

Research Collection School Of Computing and Information Systems

Spectrum Based Fault Localization (SBFL) is a statistical approach to identify faulty code within a program given a program spectra (i.e., records of program elements executed by passing and failing test cases). Several SBFL techniques have been proposed over the years, but most evaluations of those techniques were done only on Java and C programs, and frequently involve artificial faults. Considering the current popularity of Python, indicated by the results of the Stack Overflow survey among developers in 2020, it becomes increasingly important to understand how SBFL techniques perform on Python projects. However, this remains an understudied topic. In this …


Adaptive Fairness Improvement Based Causality Analysis, Mengdi ZHANG, Jun SUN 2022 Singapore Management University

Adaptive Fairness Improvement Based Causality Analysis, Mengdi Zhang, Jun Sun

Research Collection School Of Computing and Information Systems

Given a discriminating neural network, the problem of fairness improvement is to systematically reduce discrimination without significantly scarifies its performance (i.e., accuracy). Multiple categories of fairness improving methods have been proposed for neural networks, including pre-processing, in-processing and postprocessing. Our empirical study however shows that these methods are not always effective (e.g., they may improve fairness by paying the price of huge accuracy drop) or even not helpful (e.g., they may even worsen both fairness and accuracy). In this work, we propose an approach which adaptively chooses the fairness improving method based on causality analysis. That is, we choose the …


Large-Scale Analysis Of Non-Termination Bugs In Real-World Oss Projects, Xiuhan SHI, Xiaofei XIE, Yi LI, Yao ZHANG, Sen CHEN, Xiaohong LI 2022 Singapore Management University

Large-Scale Analysis Of Non-Termination Bugs In Real-World Oss Projects, Xiuhan Shi, Xiaofei Xie, Yi Li, Yao Zhang, Sen Chen, Xiaohong Li

Research Collection School Of Computing and Information Systems

Termination is a crucial program property. Non-termination bugs can be subtle to detect and may remain hidden for long before they take effect. Many real-world programs still suffer from vast consequences (e.g., no response) caused by non-termination bugs. As a classic problem, termination proving has been studied for many years. Many termination checking tools and techniques have been developed and demonstrated effectiveness on existing wellestablished benchmarks. However, the capability of these tools in finding practical non-termination bugs has yet to be tested on real-world projects. To fill in this gap, in this paper, we conducted the first large-scale empirical study …


Which Neural Network Makes More Explainable Decisions? An Approach Towards Measuring Explainability, Mengdi ZHANG, Jun SUN, Jingyi WANG 2022 Singapore Management University

Which Neural Network Makes More Explainable Decisions? An Approach Towards Measuring Explainability, Mengdi Zhang, Jun Sun, Jingyi Wang

Research Collection School Of Computing and Information Systems

Neural networks are getting increasingly popular thanks to their exceptional performance in solving many real-world problems. At the same time, they are shown to be vulnerable to attacks, difficult to debug and subject to fairness issues. To improve people’s trust in the technology, it is often necessary to provide some human-understandable explanation of neural networks’ decisions, e.g., why is that my loan application is rejected whereas hers is approved? That is, the stakeholder would be interested to minimize the chances of not being able to explain the decision consistently and would like to know how often and how easy it …


Itiger: An Automatic Issue Title Generation Tool, Ting ZHANG, Ivana Clairine IRSAN, Thung Ferdian, DongGyun HAN, David LO, Lingxiao JIANG 2022 Singapore Management University

Itiger: An Automatic Issue Title Generation Tool, Ting Zhang, Ivana Clairine Irsan, Thung Ferdian, Donggyun Han, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

In both commercial and open-source software, bug reports or issues are used to track bugs or feature requests. However, the quality of issues can differ a lot. Prior research has found that bug reports with good quality tend to gain more attention than the ones with poor quality. As an essential component of an issue, title quality is an important aspect of issue quality. Moreover, issues are usually presented in a list view, where only the issue title and some metadata are present. In this case, a concise and accurate title is crucial for readers to grasp the general concept …


Mando-Guru: Vulnerability Detection For Smart Contract Source Code By Heterogeneous Graph Embeddings, Huu Hoang NGUYEN, Nhat Minh NGUYEN, Hong-Phuc DOAN, Zahrai AHMADI, Thanh Nam DOAN, Lingxiao JIANG 2022 Singapore Management University

Mando-Guru: Vulnerability Detection For Smart Contract Source Code By Heterogeneous Graph Embeddings, Huu Hoang Nguyen, Nhat Minh Nguyen, Hong-Phuc Doan, Zahrai Ahmadi, Thanh Nam Doan, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Smart contracts are increasingly used with blockchain systems for high-value applications. It is highly desired to ensure the quality of smart contract source code before they are deployed. This paper proposes a new deep learning-based tool, MANDO-GURU, that aims to accurately detect vulnerabilities in smart contracts at both coarse-grained contract-level and fine-grained line-level. Using a combination of control-flow graphs and call graphs of Solidity code, we design new heterogeneous graph attention neural networks to encode more structural and potentially semantic relations among different types of nodes and edges of such graphs and use the encoded embeddings of the graphs and …


Recipegen++: An Automated Trigger Action Programs Generator, IMAM NUR BANI YUSUF, DIYANAH BINTE ABDUL JAMAL, Lingxiao JIANG, David LO 2022 Singapore Management University

Recipegen++: An Automated Trigger Action Programs Generator, Imam Nur Bani Yusuf, Diyanah Binte Abdul Jamal, Lingxiao Jiang, David Lo

Research Collection School Of Computing and Information Systems

Trigger Action Programs (TAPs) are event-driven rules that allow users to automate smart-devices and internet services. Users can write TAPs by specifying triggers and actions from a set of predefined channels and functions. Despite its simplicity, composing TAPs can still be challenging for users due to the enormous search space of available triggers and actions. The growing popularity of TAPs is followed by the increasing number of supported devices and services, resulting in a huge number of possible combinations between triggers and actions. Motivated by such a fact, we improve our prior work and propose RecipeGen++, a deep-learning-based approach that …


Autopruner: Transformer-Based Call Graph Pruning, Cong Thanh LE, Hong Jin KANG, Truong Giang NGUYEN, Stefanus AGUS HARYONO, David LO, Xuan-Bach D. LE, Huynh Quyet THANG 2022 Singapore Management University

Autopruner: Transformer-Based Call Graph Pruning, Cong Thanh Le, Hong Jin Kang, Truong Giang Nguyen, Stefanus Agus Haryono, David Lo, Xuan-Bach D. Le, Huynh Quyet Thang

Research Collection School Of Computing and Information Systems

Constructing a static call graph requires trade-offs between soundness and precision. Program analysis techniques for constructing call graphs are unfortunately usually imprecise. To address this problem, researchers have recently proposed call graph pruning empowered by machine learning to post-process call graphs constructed by static analysis. A machine learning model is built to capture information from the call graph by extracting structural features for use in a random forest classifier. It then removes edges that are predicted to be false positives. Despite the improvements shown by machine learning models, they are still limited as they do not consider the source code …


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