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Articles 2041 - 2070 of 4322

Full-Text Articles in Computer Sciences

Data Scientist’S Analysis Toolbox: Comparison Of Python, R, And Sas Performance, Jim Brittain, Mariana Cendon, Jennifer Nizzi, John Pleis Jul 2018

Data Scientist’S Analysis Toolbox: Comparison Of Python, R, And Sas Performance, Jim Brittain, Mariana Cendon, Jennifer Nizzi, John Pleis

SMU Data Science Review

A quantitative analysis will be performed on experiments utilizing three different tools used for Data Science. The analysis will include replication of analysis along with comparisons of code length, output, and results. Qualitative data will supplement the quantitative findings. The conclusion will provide data support guidance on the correct tool to use for common situations in the field of Data Science.

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Lightweight Multilingual Software Analysis, Damian Lyons, Anne Marie Bogar, David Baird Jul 2018

Lightweight Multilingual Software Analysis, Damian Lyons, Anne Marie Bogar, David Baird

Faculty Publications

Developer preferences, language capabilities and the persistence of older languages contribute to the trend that large software codebases are often multilingual – that is, written in more than one computer language. While developers can leverage monolingual software development tools to build software components, companies are faced with the problem of managing the resultant large, multilingual codebases to address issues with security, efficiency, and quality metrics. The key challenge is to address the opaque nature of the language interoperability interface: one language calling procedures in a second (which may call a third, or even back to the first), resulting in a …

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Searching For Relevant Lessons Learned Using Hybrid Information Retrieval Classifiers: A Case Study In Software Engineering, Tamer Mohamed Abdellatif Mohamed, Luiz Fernando Capretz, Danny Ho Jul 2018

Searching For Relevant Lessons Learned Using Hybrid Information Retrieval Classifiers: A Case Study In Software Engineering, Tamer Mohamed Abdellatif Mohamed, Luiz Fernando Capretz, Danny Ho

Electrical and Computer Engineering Publications

The lessons learned (LL) repository is one of the most valuable sources of knowledge for a software organization. It can provide distinctive guidance regarding previous working solutions for historical software management problems, or former success stories to be followed. However, the unstructured format of the LL repository makes it difficult to search using general queries, which are manually inputted by project managers (PMs). For this reason, this repository may often be overlooked despite the valuable information it provides. Since the LL repository targets PMs, the search method should be domain specific rather than generic as in the case of general …

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Kbase: The United States Department Of Energy Systems Biology Knowledgebase, Adam P. Arkin, Robert W. Cottingham, Christopher S. Henry, Nomi L. Harris, Rick L. Stevens, Sergei Maslov, Doreen Ware, Fernando Perez, Shane Canon, Michael W. Sneddon, Matthew L. Henderson, William J. Riehl, Dan Murphy-Olson, Stephen Y. Chan, Roy T. Kamimura, Sunita Kumari, Meghan M. Drake, Thomas S. Brettin, Elizabeth M. Glass, Dylan Chivian, Dan Gunter, David J. Weston, Benjamin H. Allen, Jason Baumohl, Nathan L. Tintle Jul 2018

Kbase: The United States Department Of Energy Systems Biology Knowledgebase, Adam P. Arkin, Robert W. Cottingham, Christopher S. Henry, Nomi L. Harris, Rick L. Stevens, Sergei Maslov, Doreen Ware, Fernando Perez, Shane Canon, Michael W. Sneddon, Matthew L. Henderson, William J. Riehl, Dan Murphy-Olson, Stephen Y. Chan, Roy T. Kamimura, Sunita Kumari, Meghan M. Drake, Thomas S. Brettin, Elizabeth M. Glass, Dylan Chivian, Dan Gunter, David J. Weston, Benjamin H. Allen, Jason Baumohl, Nathan L. Tintle

Faculty Work Comprehensive List

No abstract provided.

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Analysis Of Public Transportation Patterns In A Densely Populated City With Station-Based Shared Bikes, Di Wang, Evan Wu, Ah-Hwee Tan Jul 2018

Analysis Of Public Transportation Patterns In A Densely Populated City With Station-Based Shared Bikes, Di Wang, Evan Wu, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Densely populated cities face great challenges of high transportation demand and limited physical space. Thus, in these cities, the public transportation system is heavily relied on. Conventional public transportation modes such as bus, taxi and subway have been globally deployed over the past century. In the last decade, a new type of public transportation mode, shared bike, emerged in many cities. These shared bikes are deployed by either government-regulated or profit-driven companies and are either station-based or station-less. Nonetheless, all of them are designed to better solve the last-mile problem in densely populated cities as complements to the conventional public …

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Autonomous Agents In Snake Game Via Deep Reinforcement Learning, Zhepei Wei, Di Wang, Ming Zhang, Ah-Hwee Tan, Chunyan Miao, You Zhou Jul 2018

Autonomous Agents In Snake Game Via Deep Reinforcement Learning, Zhepei Wei, Di Wang, Ming Zhang, Ah-Hwee Tan, Chunyan Miao, You Zhou

Research Collection School Of Computing and Information Systems

Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has become a commonly adopted method to enable the agents to learn complex control policies in various video games. However, similar approaches may still need to be improved when applied to more challenging scenarios, where reward signals are sparse and delayed. In this paper, we develop a refined DRL model to enable our autonomous agent to play the classical Snake Game, whose constraint gets stricter as the game progresses. Specifically, we employ a convolutional neural network (CNN) trained with a variant of Q-learning. Moreover, we …

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Technology-Enabled Medication Adherence For Seniors Living In The Community: Experiences, Lessons, And The Road Ahead, Hwee Xian Tan, Hwee-Pink Tan, Huiguang Liang Jul 2018

Technology-Enabled Medication Adherence For Seniors Living In The Community: Experiences, Lessons, And The Road Ahead, Hwee Xian Tan, Hwee-Pink Tan, Huiguang Liang

Research Collection School Of Computing and Information Systems

Medication non-adherence in seniors can lead to severe health complications, including morbidity, mortality and decreased quality of life. In view of ageing populations worldwide, there is significant interest among the healthcare sector and researchers to improve medication adherence rates for seniors. However, existing studies in the literature focus primarily on identifying the predictors of medication non-adherence. In this paper, we present our work on technology-enabled medication adherence for 24 community-dwelling seniors over a period of more than 2 years. We leverage Internet of Things (IoT) devices to track inferred medication consumption in the seniors’ homes, and provide quasi real-time alerts …

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Identifying Elderlies At Risk Of Becoming More Depressed With Internet-Of-Things, Jiajue Ou, Huiguang Liang, Hwee Xian Tan Jul 2018

Identifying Elderlies At Risk Of Becoming More Depressed With Internet-Of-Things, Jiajue Ou, Huiguang Liang, Hwee Xian Tan

Research Collection School Of Computing and Information Systems

Depression in the elderly is common and dangerous. Current methods to monitor elderly depression, however, are costly, time-consuming and inefficient. In this paper, we present a novel depression-monitoring system that infers an elderly’s changes in depression level based on his/her activity patterns, extracted from wireless sensor data. To do so, we build predictive models to learn the relationship between depression level changes and behaviors using historical data. We also deploy the system for a group of elderly, in their homes, and run the experiments for more than one year. Our experimental study gives encouraging results, suggesting that our IoT system …

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Unobtrusive Detection Of Frailty In Older Adults, Nadee Goonawardene, Hwee-Pink Tan, Lee Buay Tan Jul 2018

Unobtrusive Detection Of Frailty In Older Adults, Nadee Goonawardene, Hwee-Pink Tan, Lee Buay Tan

Research Collection School Of Computing and Information Systems

Sensor technologies have gained attention as an effective means to monitor physical and mental wellbeing of elderly. In this study, we examined the possibility of using passive in-home sensors to detect frailty in older adults based on their day-to-day in-home living pattern. The sensor-based elderly monitoring system consists of PIR motion sensors and a door contact sensor attached to the main door. A set of pre-defined features associated with elderly’s day-to-day living patterns were derived based on sensor data of 46 elderly gathered over two different time periods. A series of feature vectors depicting different behavioral aspects were derived to …

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Experiences & Challenges With Server-Side Wifi Indoor Localization Using Existing Infrastructure, Dheryta Jaisinghani, Rajesh Krishna Balan, Vinayak Naik, Archan Misra, Youngki Lee Jul 2018

Experiences & Challenges With Server-Side Wifi Indoor Localization Using Existing Infrastructure, Dheryta Jaisinghani, Rajesh Krishna Balan, Vinayak Naik, Archan Misra, Youngki Lee

Research Collection School Of Computing and Information Systems

Real-world deployments of WiFi-based indoor localization in large public venues are few and far between as most state-of-the-art solutions require either client or infrastructure-side changes. Hence, even though high location accuracy is possible with these solutions, they are not practical due to cost and/or client adoption reasons. Majority of the public venues use commercial controller-managed WLAN solutions, that neither allow client changes nor infrastructure changes. In fact, for such venues we have observed highly heterogeneous devices with very low adoption rates for client-side apps. In this paper, we present our experiences in deploying a scalable location system for such venues. …

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Deep Specification Mining, Tien-Duy B. Le, David Lo Jul 2018

Deep Specification Mining, Tien-Duy B. Le, David Lo

Research Collection School Of Computing and Information Systems

Formal specifications are essential but usually unavailable in software systems. Furthermore, writing these specifications is costly and requires skills from developers. Recently, many automated techniques have been proposed to mine specifications in various formats including finite-state automaton (FSA). However, more works in specification mining are needed to further improve the accuracy of the inferred specifications. In this work, we propose Deep Specification Miner (DSM), a new approach that performs deep learning for mining FSA-based specifications. Our proposed approach uses test case generation to generate a richer set of execution traces for training a Recurrent Neural Network Based Language Model (RNNLM). …

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Summarizing Source Code With Transferred Api Knowledge, Xing Hu, Ge Li, Xin Xia, David Lo, Shuai Lu, Zhi Jin Jul 2018

Summarizing Source Code With Transferred Api Knowledge, Xing Hu, Ge Li, Xin Xia, David Lo, Shuai Lu, Zhi Jin

Research Collection School Of Computing and Information Systems

Code summarization, aiming to generate succinct natural language description of source code, is extremely useful for code search and code comprehension. It has played an important role in software maintenance and evolution. Previous approaches generate summaries by retrieving summaries from similar code snippets. However, these approaches heavily rely on whether similar code snippets can be retrieved, how similar the snippets are, and fail to capture the API knowledge in the source code, which carries vital information about the functionality of the source code. In this paper, we propose a novel approach, named TL-CodeSum, which successfully uses API knowledge learned in …

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Characterizing Common And Domain-Specific Package Bugs: A Case Study On Ubuntu, Xiaoxue Ren, Qiao Huang, Xin Xia, Zhenchang Xing, Lingfeng Bao, David Lo Jul 2018

Characterizing Common And Domain-Specific Package Bugs: A Case Study On Ubuntu, Xiaoxue Ren, Qiao Huang, Xin Xia, Zhenchang Xing, Lingfeng Bao, David Lo

Research Collection School Of Computing and Information Systems

Ubuntu is an open source software platform that runs everywhere from the smartphone, the tablet and the PC to the server and the cloud. In Ubuntu, there are many self-contained or third-party software packages for different use, and a bug report in Ubuntu could affect one or more packages simultaneously. Identifying the common package bugs in Ubuntu can help both developers and users better understand the packages they are developing or using, and also provide further guidelines to developers of similar packages in the future. In this paper, we perform a large-scale empirical study of common package bugs on Ubuntu …

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A Survey On Sensor Calibration In Air Pollution Monitoring Deployments, Balz Maah, Zimu Zhou, Lothar Thiele Jul 2018

A Survey On Sensor Calibration In Air Pollution Monitoring Deployments, Balz Maah, Zimu Zhou, Lothar Thiele

Research Collection School Of Computing and Information Systems

Air pollution is a major concern for public health and urban environments. Conventional air pollution monitoring systems install a few highly accurate, expensive stations at representative locations. Their sparse coverage and low spatial resolution are insufficient to quantify urban air pollution and its impacts on human health and environment. Advances in lowcost portable air pollution sensors have enabled air pollution monitoring deployments at scale to measure air pollution at high spatiotemporal resolution. However, it is challenging to ensure the accuracy of these low-cost sensor deployments because the sensors are more error-prone than high-end sensing infrastructures and they are often deployed …

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Towards 'Verifying' A Water Treatment System, Jingyi Wang, Jun Sun, Yifan Jia, Shengchao Qin, Zhiwu Xu Jul 2018

Towards 'Verifying' A Water Treatment System, Jingyi Wang, Jun Sun, Yifan Jia, Shengchao Qin, Zhiwu Xu

Research Collection School Of Computing and Information Systems

Modeling and verifying real-world cyber-physical systems is challenging, which is especially so for complex systems where manually modeling is infeasible. In this work, we report our experience on combining model learning and abstraction refinement to analyze a challenging system, i.e., a real-world Secure Water Treatment system (SWaT). Given a set of safety requirements, the objective is to either show that the system is safe with a high probability (so that a system shutdown is rarely triggered due to safety violation) or not. As the system is too complicated to be manually modeled, we apply latest automatic model learning techniques to …

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Compositional Reasoning For Shared-Variable Concurrent Programs, Fuyuan Zhang, Yongwang Zhao, David Sanan, Yang Liu, Alwen Tiu, Shang-Wei Lin, Jun Sun Jul 2018

Compositional Reasoning For Shared-Variable Concurrent Programs, Fuyuan Zhang, Yongwang Zhao, David Sanan, Yang Liu, Alwen Tiu, Shang-Wei Lin, Jun Sun

Research Collection School Of Computing and Information Systems

Scalable and automatic formal verification for concurrent systems is always demanding. In this paper, we propose a verification framework to support automated compositional reasoning for concurrent programs with shared variables. Our framework models concurrent programs as succinct automata and supports the verification of multiple important properties. Safety verification and simulations of succinct automata are parallel compositional, and safety properties of succinct automata are preserved under refinements. We generate succinct automata from infinite state concurrent programs in an automated manner. Furthermore, we propose the first automated approach to checking rely-guarantee based simulations between infinite state concurrent programs. We have prototyped our …

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Stackelberg Security Games: Looking Beyond A Decade Of Success, Arunesh Sinha, Fei Fang, Bo An, Christopher Kiekintveld, Milind Tambe Jul 2018

Stackelberg Security Games: Looking Beyond A Decade Of Success, Arunesh Sinha, Fei Fang, Bo An, Christopher Kiekintveld, Milind Tambe

Research Collection School Of Computing and Information Systems

The Stackelberg Security Game (SSG) model has been immensely influential in security research since it was introduced roughly a decade ago. Furthermore, deployed SSG-based applications are one of most successful examples of game theory applications in the real world. We present a broad survey of recent technical advances in SSG and related literature, and then look to the future by highlighting the new potential applications and open research problems in SSG.

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A Unified Approach To Route Planning For Shared Mobility, Yongxin Tong, Yuxiang Zeng, Zimu Zhou, Lei Chen, Jieping Ye, Ke Xu Jul 2018

A Unified Approach To Route Planning For Shared Mobility, Yongxin Tong, Yuxiang Zeng, Zimu Zhou, Lei Chen, Jieping Ye, Ke Xu

Research Collection School Of Computing and Information Systems

There has been a dramatic growth of shared mobility applications such as ride-sharing, food delivery and crowdsourced parcel delivery. Shared mobility refers to transportation services that are shared among users, where a central issue is route planning. Given a set of workers and requests, route planning finds for each worker a route, i.e., a sequence of locations to pick up and drop off passengers/parcels that arrive from time to time, with different optimization objectives. Previous studies lack practicability due to their conflicted objectives and inefficiency in inserting a new request into a route, a basic operation called insertion. In this …

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Using Eeg-Validated Music Emotion Recognition Techniques To Classify Multi-Genre Popular Music For Therapeutic Purposes, Dejoy Shastikk Kumaran Jun 2018

Using Eeg-Validated Music Emotion Recognition Techniques To Classify Multi-Genre Popular Music For Therapeutic Purposes, Dejoy Shastikk Kumaran

The International Student Science Fair 2018

Music is observed to possess significant beneficial effects to human mental health, especially for patients undergoing therapy and older adults. Prior research focusing on machine recognition of the emotion music induces by classifying low-level music features has utilized subjective annotation to label data for classification. We validate this approach by using an electroencephalography-based approach to cross-check the predictions of music emotion made with the predictions from low-level music feature data as well as collected subjective annotation data. Collecting 8-channel EEG data from 10 participants listening to segments of 40 songs from 5 different genres, we obtain a subject-independent classification accuracy …

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Using Eeg-Validated Music Emotion Recognition Techniques To Classify Multi-Genre Popular Music For Therapeutic Purposes, Dejoy Shastikk Kumaran Jun 2018

Using Eeg-Validated Music Emotion Recognition Techniques To Classify Multi-Genre Popular Music For Therapeutic Purposes, Dejoy Shastikk Kumaran

The International Student Science Fair 2018

Music is observed to possess significant beneficial effects to human mental health, especially for patients undergoing therapy and older adults. Prior research focusing on machine recognition of the emotion music induces by classifying low-level music features has utilized subjective annotation to label data for classification. We validate this approach by using an electroencephalography-based approach to cross-check the predictions of music emotion made with the predictions from low-level music feature data as well as collected subjective annotation data. Collecting 8-channel EEG data from 10 participants listening to segments of 40 songs from 5 different genres, we obtain a subject-independent classification accuracy …

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An Integrated Approach For Effective Injection Vulnerability Analysis Of Web Applications Through Security Slicing And Hybrid Constraint Solving, Julian Thome, Lwin Khin Shar, Domenico Bianculli, Lionel Briand Jun 2018

An Integrated Approach For Effective Injection Vulnerability Analysis Of Web Applications Through Security Slicing And Hybrid Constraint Solving, Julian Thome, Lwin Khin Shar, Domenico Bianculli, Lionel Briand

Research Collection School Of Computing and Information Systems

Malicious users can attack Web applications by exploiting injection vulnerabilities in the source code. This work addresses the challenge of detecting injection vulnerabilities in the server-side code of Java Web applications in a scalable and effective way. We propose an integrated approach that seamlessly combines security slicing with hybrid constraint solving; the latter orchestrates automata-based solving with meta-heuristic search. We use static analysis to extract minimal program slices relevant to security from Web programs and to generate attack conditions. We then apply hybrid constraint solving to determine the satisfiability of attack conditions and thus detect vulnerabilities. The experimental results, using …

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Static Analysis Of Context Leaks In Android Applications, Flavio Toffalini, Jun Sun, Martín Cohoa Jun 2018

Static Analysis Of Context Leaks In Android Applications, Flavio Toffalini, Jun Sun, Martín Cohoa

Research Collection School Of Computing and Information Systems

Android native applications, written in Java and distributed in APK format, are widely used in mobile devices. Their specific pattern of use lets the operating system control the creation and destruction of key resources, such as activities and services (contexts). Programmers are not supposed to interfere with such lifecycle events. Otherwise contexts might be leaked, i.e. they will never be deallocated from memory, or be deallocated too late, leading to memory exhaustion and frozen applications. In practice, it is easy to write incorrect code, which hinders garbage collection of contexts and subsequently leads to context leakage.In this work, we present …

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Towards Optimal Concolic Testing, Xinyu Wang, Jun Sun, Zhenbang Chen, Peixin Zhang, Jingyi Wang, Yun Lin Jun 2018

Towards Optimal Concolic Testing, Xinyu Wang, Jun Sun, Zhenbang Chen, Peixin Zhang, Jingyi Wang, Yun Lin

Research Collection School Of Computing and Information Systems

Concolic testing integrates concrete execution (e.g., random testing) and symbolic execution for test case generation. It is shown to be more cost-effective than random testing or symbolic execution sometimes. A concolic testing strategy is a function which decides when to apply random testing or symbolic execution, and if it is the latter case, which program path to symbolically execute. Many heuristics-based strategies have been proposed. It is still an open problem what is the optimal concolic testing strategy. In this work, we make two contributions towards solving this problem. First, we show the optimal strategy can be defined based on …

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Analysing Popularity Of Software Testing Careers In Canada, Luiz Fernando Capretz, Pradeep Waychal, Sachin Pardeshi Jun 2018

Analysing Popularity Of Software Testing Careers In Canada, Luiz Fernando Capretz, Pradeep Waychal, Sachin Pardeshi

Electrical and Computer Engineering Publications

Software testing is critical to prevent software failures. Therefore, research has been carried out in testing but that is largely limited to the processand technology dimensions and has not sufficiently addressed the human dimension. Even though there are reports about inadequacies of testing professionals and their skills, only a few studies have tackled the problem. Therefore, we decided to explore the human dimension. We started with the basic problem that plagues the testing profession, the shortage of talent, by asking why do students and professionals are reluctant to consider testing careers, what can be done about that, and is the …

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Funqual: User-Defined, Statically-Checked Call Graph Constraints In C++, Andrew P. Nelson Jun 2018

Funqual: User-Defined, Statically-Checked Call Graph Constraints In C++, Andrew P. Nelson

Master's Theses

Static analysis tools can aid programmers by reporting potential programming mistakes prior to the execution of a program. Funqual is a static analysis tool that reads C++17 code ``in the wild'' and checks that the function call graph follows a set of rules which can be defined by the user. This sort of analysis can help the programmer to avoid errors such as accidentally calling blocking functions in time-sensitive contexts or accidentally allocating memory in heap-sensitive environments. To accomplish this, we create a type system whereby functions can be given user-defined type qualifiers and where users can define their own …

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The Effect Of Endgame Tablebases On Modern Chess Engines, Christopher D. Peterson Jun 2018

The Effect Of Endgame Tablebases On Modern Chess Engines, Christopher D. Peterson

Computer Engineering

Modern chess engines have the ability to augment their evaluation by using massive tables containing billions of positions and their memorized solutions. This report examines the importance of these tables to better understand the circumstances under which they should be used. The analysis conducted in this paper empirically examines differences in size and speed of memorized positions and their impacts on engine strength. Using this technique, situations where memorized tables improve play (and situations where they do not) are discovered.

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Advanced Malware Detection For Android Platform, Ke Xu Jun 2018

Advanced Malware Detection For Android Platform, Ke Xu

Dissertations and Theses Collection (Open Access)

In the first quarter of 2018, 75.66% of smartphones sales were devices running An- droid. Due to its popularity, cyber-criminals have increasingly targeted this ecosys- tem. Malware running on Android severely violates end users security and privacy, allowing many attacks such as defeating two factor authentication of mobile bank- ing applications, capturing real-time voice calls and leaking sensitive information. In this dissertation, I describe the pieces of work that I have done to effectively de- tect malware on Android platform, i.e., ICC-based malware detection system (IC- CDetector), multi-layer malware detection system (DeepRefiner), and self-evolving and scalable malware detection system (DroidEvolver) …

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Jupyterlab_Voyager: A Data Visualization Enhancement In Jupyterlab, Ji Zhang Jun 2018

Jupyterlab_Voyager: A Data Visualization Enhancement In Jupyterlab, Ji Zhang

Master's Theses

With the emergence of big data, scientific data analysis and visualization (DAV) tools are critical components of the data science software ecosystem; the usability of these tools is becoming extremely important to facilitate next-generation scientific discoveries. JupyterLab has been considered as one of the best polyglot, web-based, open-source data science tools. As the next phase of extensible interface for the classic iPython Notebooks, this tool supports interactive data science and scientific computing across multiple programming languages with great performances. Despite these advantages, previous heuristics evaluation studies have shown that JupyterLab has some significant flaws in the data visualization side. The …

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Table Of Interest: Activity Recognition And Behaviour Analysis Using A Battery Lesswearable Sensor, Dibyanshu Jaiswal, Andrew Gigie, Tapas Chakravarty, Avik Ghose, Archan Misra Jun 2018

Table Of Interest: Activity Recognition And Behaviour Analysis Using A Battery Lesswearable Sensor, Dibyanshu Jaiswal, Andrew Gigie, Tapas Chakravarty, Avik Ghose, Archan Misra

Research Collection School Of Computing and Information Systems

Energy overheads continue to be a major impediment for wearable based activity recognition systems. We proposed a hybrid approach, which combines wearable-based human sensing with object interaction tracking, for robust detection of ADLs in smart homes. Our proposed framework includes: (a) battery less, low sampling rate, wearable RF sensor tags, that are powered intermittently by an RFID reader, and (b) additional passive RF tags, mounted on daily use objects, that capture the presence and use of specific objects while performing such ADLs. Using an initial experimental set up, we show the ability to recognize activities like eating, typing and reading, …

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Inference Of Development Activities From Interaction With Uninstrumented Applications, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Ahmed E. Hassan Jun 2018

Inference Of Development Activities From Interaction With Uninstrumented Applications, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Ahmed E. Hassan

Research Collection School Of Computing and Information Systems

Studying developers’ behavior in software development tasks is crucial for designing effective techniques and tools to support developers’ daily work. In modern software development, developers frequently use different applications including IDEs, Web Browsers, documentation software (such as Office Word, Excel, and PDF applications), and other tools to complete their tasks. This creates significant challenges in collecting and analyzing developers’ behavior data. Researchers usually instrument the software tools to log developers’ behavior for further studies. This is feasible for studies on development activities using specific software tools. However, instrumenting all software tools commonly used in real work settings is difficult and …

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