Computer Sciences Commons^™

Recommending Frequently Encountered Bugs, Yun Zhang, David Lo, Xin Xia, Jing Jiang, Jianling Sun

Research Collection School Of Computing and Information Systems

Developers introduce bugs during software development which reduce software reliability. Many of these bugs are commonly occurring and have been experienced by many other developers. Informingdevelopers, especially novice ones, about commonly occurring bugsin a domain of interest (e.g., Java), can help developers comprehendprogram and avoid similar bugs in the future. Unfortunately, information about commonly occurring bugs are not readily available. Toaddress this need, we propose a novel approach named RFEB whichrecommends frequently encountered bugs (FEBugs) that may affectmany other developers. RFEB analyzes Stack Overflow which is thelargest software engineering-specific Q&A communities. Amongthe plenty of questions posted in Stack Overflow, many …

Deep Code Comment Generation, Xing Hu, Ge Li, Xin Xia, David Lo, Zhi Jin

Research Collection School Of Computing and Information Systems

During software maintenance, code comments help developerscomprehend programs and reduce additional time spent on readingand navigating source code. Unfortunately, these comments areoften mismatched, missing or outdated in the software projects.Developers have to infer the functionality from the source code.This paper proposes a new approach named DeepCom to automatically generate code comments for Java methods. The generatedcomments aim to help developers understand the functionalityof Java methods. DeepCom applies Natural Language Processing(NLP) techniques to learn from a large code corpus and generatescomments from learned features. We use a deep neural networkthat analyzes structural information of Java methods for bettercomments generation. We conduct …

Anflo: Detecting Anomalous Sensitive Information Flows In Android Apps, Biniam Fisseha Demissie, Mariano Ceccato, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Smartphone apps usually have access to sensitive user data such as contacts, geo-location, and account credentials and they might share such data to external entities through the Internet or with other apps. Confidentiality of user data could be breached if there are anomalies in the way sensitive data is handled by an app which is vulnerable or malicious. Existing approaches that detect anomalous sensitive data flows have limitations in terms of accuracy because the definition of anomalous flows may differ for different apps with different functionalities; it is normal for “Health” apps to share heart rate information through the Internet …

Learning From Mutants: Using Code Mutation To Learn And Monitor Invariants Of A Cyber-Physical System, Yuqi Chen, Christopher M. Poskitt, Jun Sun

Research Collection School Of Computing and Information Systems

Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage. With access to data logs and a model of the CPS, the physical effects of an attack could potentially be detected before any damage is done. Manually building a model that is accurate enough in practice, however, is extremely difficult. In this paper, we propose a novel approach for constructing models of CPS automatically, by applying supervised machine learning to data traces obtained after systematically seeding their software components with faults ("mutants"). We demonstrate the …

Budgeting In Student Life: An Educational Website, Heather Grunden

Honors Projects

An applied honors project in the form of a website prototype. The purpose of this website is to introduce college students to the concept of budgeting and to teach them the core steps of creating their own budget, since many existing budgeting applications are pay-to-use, and the free options tend to have little to no instruction.

Fit Buddy Prototype And Ksugo Mobile App, Albert Lim

KSU Journey Honors College Capstones and Theses

The purpose of my Honors Capstone project is to deliver a mobile app prototype that is focused on improving the student experience at Kennesaw State University (KSU), called Fit Buddy. There are three key concepts that will be covered in this Proof of Concept project: social networking, fitness, and IoT (Internet of Things) usages.

Additionally, the purpose of the CS Capstone project is to deliver a mobile app that is focused on improving the faculty, student, and guest experience at KSU, called KSUGo. The primary objective and purpose in creating this stems from KSU’s many resourceful outlets, which we seek …

Creating A Reproducible Metadata Transformation Pipeline Using Technology Best Practices, Cara Key, Mike Waugh

Digital Initiatives Symposium

Over the course of two years, a team of librarians and programmers from LSU Libraries migrated the 186 collections of the Louisiana Digital Library from OCLC's CONTENTdm platform over to the open-source Islandora platform.

Early in the process, the team understood the value of creating a reproducible metadata transformation pipeline, because there were so many unknowns at the beginning of the process along with the certainty that mistakes would be made. This presentation will describe how the team used innovative and collaborative tools, such as Trello, Ansible, Vagrant, VirtualBox, git and GitHub to accomplish the task.

Seismology And Volcanology: Exploration Of Volcanoes, Long-Periods, And Machines - Predicting Volcano Eruption Using Signature Seismic Data, Kyle Killion, Rajeev Kumar, Celia J. Taylor, Gabriele Morra

SMU Data Science Review

Abstract. Seismo-volcanologists manually isolate and verify long-period waves and Strombolian events using seismic and acoustic waves. This is a very detailed and time-consuming process. This project is to employ machine learning algorithms to find models which locate long-period and Strombolian signatures automatically. By comparing the timing of seismic and acoustic waves, clustering techniques effectively isolated big volcanic events and aided in the further refinement of techniques to capture the hundreds of typical daily Strombolian events at Villarrica volcano. Within the research, we utilized the unsupervised machine learning environment to locate a group of signatures for customizing machine learned long-period signature …

Lee Honors College Mobile Application, James Ward

Honors Theses

In the spring of 2018 three Computer Science students Benjamin Campbell, James Ward, and Peter Shutt created a mobile application. This app was developed over the span of two semesters for their senior design project; a capstone to their degrees.

Their client, The Lee Honors College at Western Michigan University --referred to as LHC and WMU respectively hereafter-- has a plethora of academic and social information, and a large demand for access to it. This information includes building hours, contact information, health resources, a LHC specific course catalog, social media posts, event descriptions, and much more. The volume of information, …

Proactive Empirical Assessment Of New Language Feature Adoption Via Automated Refactoring: The Case Of Java 8 Default Methods, Raffi Khatchadourian, Hidehiko Masuhara

Publications and Research

Programming languages and platforms improve over time, sometimes resulting in new language features that offer many benefits. However, despite these benefits, developers may not always be willing to adopt them in their projects for various reasons. In this paper, we describe an empirical study where we assess the adoption of a particular new language feature. Studying how developers use (or do not use) new language features is important in programming language research and engineering because it gives designers insight into the usability of the language to create meaning programs in that language. This knowledge, in turn, can drive future innovations …

Performance Characterization Of Deep Learning Models For Breathing-Based Authentication On Resource-Constrained Devices, Jagmohan Chauhan, Jathusan Rajasegaran, Surang Seneviratne, Archan Misra, Aruan Seneviratne, Youngki Lee

Research Collection School Of Computing and Information Systems

Providing secure access to smart devices such as mobiles, wearables and various other IoT devices is becoming increasinglyimportant, especially as these devices store a range of sensitive personal information. Breathing acoustics-based authentication offers a highly usable and possibly a secondary authentication mechanism for such authorized access, especially as it canbe readily applied to small form-factor devices. Executing sophisticated machine learning pipelines for such authenticationon such devices remains an open problem, given their resource limitations in terms of storage, memory and computational power. To investigate this possibility, we compare the performance of an end-to-end system for both user identification anduser verification …

Proactive Empirical Assessment Of New Language Feature Adoption Via Automated Refactoring: The Case Of Java 8 Default Methods, Raffi Khatchadourian, Hidehiko Masuhara

Publications and Research

Programming languages and platforms improve over time, sometimes resulting in new language features that offer many benefits. However, despite these benefits, developers may not always be willing to adopt them in their projects for various reasons. In this paper, we describe an empirical study where we assess the adoption of a particular new language feature. Studying how developers use (or do not use) new language features is important in programming language research and engineering because it gives designers insight into the usability of the language to create meaning programs in that language. This knowledge, in turn, can drive future innovations …

Using Github In Large Software Engineering Classes: An Exploratory Case Study, Miroslav Tushev

LSU Master's Theses

GitHub has been recently used in Software Engineering (SE) classes to facilitate col- laboration in student team projects. The underlying tenet is that the technical and social feature of GitHub can help students to communicate and collaborate more effectively as a team as well as help teachers to evaluate individual student contribution more objectively. To shed more light on this, in this case study, we explore the benefits and drawbacks of using GitHub in SE classes. Our study is conducted in a software engineering class of 91 students divided into 18 teams. Our research method includes an entry and an …

Technological Evolution In Software Engineering, Cody Miller

Engineering and Technology Management Student Projects

In all software development processes, the software must evolve in response to its environment or user needs to maintain satisfactory performance. If software doesn’t support change, it gradually becomes useless. With many organizations today, being software-centric organizations, this has huge implications for their business: evolve your software, or risk your software becoming gradually useless, and therefore, your entire business.

Technology Evolution is a highly relevant subject, Intel’s business model for the last 50 years, has been that of Moore’s Law, a hardware centric Technology Evolution model. As a Software Engineer at Intel, our business group faces a similar issue, we …

Understanding Natural Keyboard Typing Using Convolutional Neural Networks On Mobile Sensor Data, Travis Siems

Computer Science and Engineering Theses and Dissertations

Mobile phones and other devices with embedded sensors are becoming increasingly ubiquitous. Audio and motion sensor data may be able to detect information that we did not think possible. Some researchers have created models that can predict computer keyboard typing from a nearby mobile device; however, certain limitations to their experiment setup and methods compelled us to be skeptical of the models’ realistic prediction capability. We investigate the possibility of understanding natural keyboard typing from mobile phones by performing a well-designed data collection experiment that encourages natural typing and interactions. This data collection helps capture realistic vulnerabilities of the security …

Entagrec(++): An Enhanced Tag Recommendation System For Software Information Sites, Shaowei Wang, David Lo, Bogdan Vasilescu, Alexander Serebrenik

Research Collection School Of Computing and Information Systems

Software engineers share experiences with modern technologies by means of software information sites, such as Stack Overflow. These sites allow developers to label posted content, referred to as software objects, with short descriptions, known as tags. However, tags assigned to objects tend to be noisy and some objects are not well tagged. To improve the quality of tags in software information sites, we propose EnTagRec, an automatic tag recommender based on historical tag assignments to software objects and we evaluate its performance on four software information sites, Stack Overflow, Ask Ubuntu, Ask Different, and Free code. We observe that that …

Domain-Specific Cross-Language Relevant Question Retrieval, Bowen Xu, Zhenchang Xing, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Chinese developers often cannot effectively search questions in English, because they may have difficulties in translating technical words from Chinese to English and formulating proper English queries. For the purpose of helping Chinese developers take advantage of the rich knowledge base of Stack Overflow and simplify the question retrieval process, we propose an automated cross-language relevant question retrieval (CLRQR) system to retrieve relevant English questions for a given Chinese question. CLRQR first extracts essential information (both Chinese and English) from the title and description of the input Chinese question, then performs domain-specific translation of the essential Chinese information into English, …

Fast And Robust Segmentation Of White Blood Cell Images By Self-Supervised Learning, Xin Zheng, Yong Wang, Guoyou Wang, Jianguo Liu

Research Collection School Of Computing and Information Systems

A fast and accurate white blood cell (WBC) segmentation remains a challenging task, as different WBCs vary significantly in color and shape due to cell type differences, staining technique variations and the adhesion between the WBC and red blood cells. In this paper, a self-supervised learning approach, consisting of unsupervised initial segmentation and supervised segmentation refinement, is presented. The first module extracts the overall foreground region from the cell image by K-means clustering, and then generates a coarse WBC region by touching-cell splitting based on concavity analysis. The second module further uses the coarse segmentation result of the first module …

A Utp Semantics For Communicating Processes With Shared Variables And Its Formal Encoding In Pvs, Ling Shi, Yongxin Zhao, Yang Liu, Jun Sun, Jin Song Dong, Shengchao Qin

Research Collection School Of Computing and Information Systems

CSP# (communicating sequential programs) is a modelling language designed for specifying concurrent systems by integrating CSP-like compositional operators with sequential programs updating shared variables. In this work, we define an observation-oriented denotational semantics in an open environment for the CSP# language based on the UTP framework. To deal with shared variables, we lift traditional event-based traces into mixed traces which consist of state-event pairs for recording process behaviours. To capture all possible concurrency behaviours between action/channel-based communications and global shared variables, we construct a comprehensive set of rules on merging traces from processes which run in parallel/interleaving. We also define …

The Impact Of Rapid Release Cycles On The Integration Delay Of Fixed Issues, Daniel Alencar Da Costa, Shane Mcintosh, Christoph Treude, Uirá Kulesza, Ahmed E. Hassan

Research Collection School Of Computing and Information Systems

The release frequency of software projects has increased in recent years. Adopters of so-called rapid releases—short release cycles, often on the order of weeks, days, or even hours—claim that they can deliver fixed issues (i.e., implemented bug fixes and new features) to users more quickly. However, there is little empirical evidence to support these claims. In fact, our prior work shows that code integration phases may introduce delays for rapidly releasing projects—98% of the fixed issues in the rapidly releasing Firefox project had their integration delayed by at least one release. To better understand the impact that rapid release cycles …

Can Multimodal Sensing Detect And Localize Transient Events?, Kasthuri Jayarajah, Subbaraju Vigneshwaran, Noel Athaide, Lakmal Meeghapola, Andrew Tan, Archan Misra

Research Collection School Of Computing and Information Systems

With the increased focus on making cities "smarter", we see an upsurge in investment in sensing technologies embedded in the urban infrastructure. The deployment of GPS sensors aboard taxis and buses, smartcards replacing paper tickets, and other similar initiatives have led to an abundance of data on human mobility, generated at scale and available real-time. Further still, users of social media platforms such as Twitter and LBSNs continue to voluntarily share multimedia content revealing in-situ information on their respective localities. The availability of such longitudinal multimodal data not only allows for both the characterization of the dynamics of the city, …

Entagrec(++): An Enhanced Tag Recommendation System For Software Information Sites, Shawei Wang, David Lo, Bogdan Vasilescu, Alexander Serebrenik

Research Collection School Of Computing and Information Systems

Software engineers share experiences with modern technologies using software information sites, such as Stack Overflow. These sites allow developers to label posted content, referred to as software objects, with short descriptions, known as tags. Tags help to improve the organization of questions and simplify the browsing of questions for users. However, tags assigned to objects tend to be noisy and some objects are not well tagged. For instance, 14.7% of the questions that were posted in 2015 on Stack Overflow needed tag re-editing after the initial assignment. To improve the quality of tags in software information sites, we propose EnTagRec …

Combined Classifier For Cross-Project Defect Prediction: An Extended Empirical Study, Yun Zhang, David Lo, Xin Xia, Jianling Sun

Research Collection School Of Computing and Information Systems

To facilitate developers in effective allocation of their testing and debugging efforts, many software defect prediction techniques have been proposed in the literature. These techniques can be used to predict classes that are more likely to be buggy based on the past history of classes, methods, or certain other code elements. These techniques are effective provided that a sufficient amount of data is available to train a prediction model. However, sufficient training data are rarely available for new software projects. To resolve this problem, cross-project defect prediction, which transfers a prediction model trained using data from one project to another, …

Combined Classifier For Cross-Project Defect Prediction: An Extended Empirical Study, Yun Zhang, David Lo, Xin Xia, Jianling Sun

Research Collection School Of Computing and Information Systems

To help developers better allocate testing and debugging efforts, many software defect prediction techniques have been proposed in the literature. These techniques can be used to predict classes that are more likely to be buggy based on past history of buggy classes. These techniques work well as long as a sufficient amount of data is available to train a prediction model. However, there is rarely enough training data for new software projects. To deal with this problem, cross-project defect prediction, which transfers a prediction model trained using data from one project to another, has been proposed and is regarded as …

Predicting Episodes Of Non-Conformant Mobility In Indoor Environments, Kasthuri Jayarajah, Archan Misra

Research Collection School Of Computing and Information Systems

Traditional mobility prediction literature focuses primarily on improved methods to extract latent patterns from individual-specific movement data. When such predictions are incorrect, we ascribe it to 'random' or 'unpredictable' changes in a user's movement behavior. Our hypothesis, however, is that such apparently-random deviations from daily movement patterns can, in fact, of ten be anticipated. In particular, we develop a methodology for predicting Likelihood of Future Non-Conformance (LFNC), based on two central hypotheses: (a) the likelihood of future deviations in movement behavior is positively correlated to the intensity of such trajectory deviations observed in the user's recent past, and (b) the …

Real World, Large Scale Iot Systems For Community Eldercare: Experiences And Lessons Learned, Alvin Cerdena Valera, Wei Qi Lee, Hwee-Pink Tan, Hwee Xian Tan, Huiguang Liang

Research Collection School Of Computing and Information Systems

The paradigm of aging-in-place - where the elderly live and age in their own homes, independently and safely, with care provided by the community - is compelling, especially in societies that face both shortages in institutionalized eldercare resources, and rapidly-aging populations. When the number of elderly who live alone rises rapidly, support and care from their communities become increasingly critical. Internet-of-Things(IoT) technologies, particularly in-home monitoring solutions, are becoming mature. They can become the fundamental enabler for smart community eldercare. In this chapter, we share our real-world experiencesgleaned from an ongoing large-scale project on IoT-enabled community eldercare. We identify technology-centric challenges …

Vocal Programming For People With Upper-Body Motor Impairments, Lucas Rosenblatt, Patrick Carrington, Kotaro Hara, Jeffrey P. Bigham

Research Collection School Of Computing and Information Systems

Programming heavily relies on entering text using traditional QWERTY keyboards, which poses challenges for people with limited upper-body movement. Developing tools using a publicly available speech recognition API could provide a basis for keyboard free programming. In this paper, we describe our efforts in design, development, and evaluation of a voice-based IDE to support people with limited dexterity.

Feature Engineering For Machine Learning And Data Analytics, Xin Xia, David Lo

Research Collection School Of Computing and Information Systems

This chapter provides an introduction on feature generation and engineering for software analytics. Specifically, we show how domain-specifc features can be designed and used to automate three software engineering tasks: (1) detecting defective software modules (defect prediction), (2) identifying crashing mobile app release (crash release prediction), and (3) predicting who will leave a software team (developer turnover prediction). For each of the three tasks, different sets of features are extracted from a diverse set of software artifacts, and used to build predictive models.

Frame Inference For Inductive Entailment Proofs In Separation Logic, Quang Loc Le, Jun Sun, Shengchao Qin

Research Collection School Of Computing and Information Systems

Given separation logic formulae A and C, frame inference is the problem of checking whether A entails C and simultaneously inferring residual heaps. Existing approaches on frame inference do not support inductive proofs with general inductive predicates. In this work, we present an automatic frame inference approach for an expressive fragment of separation logic. We further show how to strengthen the inferred frame through predicate normalization and arithmetic inference. We have integrated our approach into an existing verification system. The experimental results show that our approach helps to establish a number of non-trivial inductive proofs which are beyond the capability …

Latency-Oriented Task Completion Via Spatial Crowdsourcing, Yuxiang Zeng, Yongxin Tong, Lei Chen, Zimu Zhou

Research Collection School Of Computing and Information Systems

Spatial crowdsourcing brings in a new approach for social media and location-based services (LBS) to collect locationspecific information via mobile users. For example, when a user checks in at a shop on Facebook, he will immediately receive and is asked to complete a set of tasks such as “what is the opening hour of the shop”. It is non-trivial to complete a set of tasks timely and accurately via spatial crowdsourcing. Since workers in spatial crowdsourcing are often transient and limited in number, these social media platforms need to properly allocate workers within the set of tasks such that all …