Software Engineering Commons^™

Software Integration In Personal Healthcare Devices And The Patient User Experience, Yassine Chahid, Patrick Slattery

Publications and Research

This study examines the current landscape and future direction of medical device hardware and software integration, focusing on how each contributes to patient care. It begins by analyzing hardware focused medical devices, such as implantable tools patients may rely on to assist with their condition, alongside diagnostic and monitoring equipment used to treat conditions in a variety of medical areas (e.g. cardiovascular conditions). It then evaluates how software is currently integrated through embedded systems, data processing, and user interfaces that support real time monitoring and clinical decision making, and how this impacts quality of care for the patient whilst minimizing …

An Empirical Framework For Evaluating Semantic Preservation Using Hugging Face, Nan Jia, Anita Raja, Raffi Khatchadourian

Publications and Research

As machine learning (ML) becomes an integral part of high-autonomy systems, it is critical to ensure the trustworthiness of learning-enabled software systems (LESS). Yet, the nondeterministic and run-time-defined semantics of ML complicate traditional software refactoring. We define semantic preservation in LESS as the property that optimizations of intelligent components do not alter the system's overall functional behavior. This paper introduces an empirical framework to evaluate semantic preservation in LESS by mining model evolution data from HuggingFace. We extract commit histories, $\textit{Model Cards}$, and performance metrics from a large number of models. To establish baselines, we conducted case studies in three …

Speculative Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian Ph.D., Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code---supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations. Our key insight is that, while DL programs typically execute sequentially, hybridizing imperative DL code resembles parallelizing sequential code in traditional systems. Inspired by this, we …

Speculative Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian Ph.D., Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code---supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations. Our key insight is that, while DL programs typically execute sequentially, hybridizing imperative DL code resembles parallelizing sequential code in traditional systems. Inspired by this, we …

Speculative Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi T. Khatchadourian Ph.D., Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code---supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations. Our key insight is that, while DL programs typically execute sequentially, hybridizing imperative DL code resembles parallelizing sequential code in traditional systems. Inspired by this, we …

Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …

Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …

Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …

Reless: A Framework For Assessing Safety In Deep Learning Systems, Nan Jia, Anita Raja, Raffi T. Khatchadourian

Publications and Research

Traditionally, software refactoring helps to improve a system's internal structure and enhance its non-functional features, such as reliability and run-time performance, while preserving external behavior including original program semantics. However, in the context of learning-enabled software systems (LESS), e.g., Machine Learning (ML) systems, it is unclear which portions of a software's semantics require preservation at the development phase. This is mainly because (a) the behavior of the LESS is not defined until run-time; and (b) the inherently iterative and non-deterministic nature of ML algorithms. Consequently, there is a knowledge gap in what refactoring truly means in the context of LESS …

Machine Learning: Face Recognition, Mohammed E. Amin

Publications and Research

This project explores the cutting-edge intersection of machine learning (ML) and face recognition (FR) technology, utilizing the OpenCV library to pioneer innovative applications in real-time security and user interface enhancement. By processing live video feeds, our system encodes visual inputs and employs advanced face recognition algorithms to accurately identify individuals from a database of photos. This integration of machine learning with OpenCV not only showcases the potential for bolstering security systems but also enriches user experiences across various technological platforms. Through a meticulous examination of unique facial features and the application of sophisticated ML algorithms and neural networks, our project …

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 …

Towards Safe Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution. We present our ongoing work on automated refactoring that assists developers in specifying whether …

Quertci: A Tool Integrating Github Issue Querying With Comment Classification, Ye Paing, Tatiana Castro Vélez, Raffi Khatchadourian

Publications and Research

Empirical Software Engineering (ESE) researchers study (open-source) project issues and the comments and threads within to discover—among others—challenges developers face when incorporating new technologies, platforms, and programming language constructs. However, such threads accumulate, becoming unwieldy and hindering any insight researchers may gain. While existing approaches alleviate this burden by classifying issue thread comments, there is a gap between searching popular open-source software repositories (e.g., those on GitHub) for issues containing particular keywords and feeding the results into a classification model. This paper demonstrates a research infrastructure tool called QuerTCI that bridges this gap by integrating the GitHub issue comment search …

Spotlight Report #6: Proffering Machine-Readable Personal Privacy Research Agreements: Pilot Project Findings For Ieee P7012 Wg, Noreen Y. Whysel, Lisa Levasseur

Publications and Research

What if people had the ability to assert their own legally binding permissions for data collection, use, sharing, and retention by the technologies they use? The IEEE P7012 has been working on an interoperability specification for machine-readable personal privacy terms to support this ability since 2018. The premise behind the work of IEEE P7012 is that people need technology that works on their behalf—i.e. software agents that assert the individual’s permissions and preferences in a machine-readable format.

Thanks to a grant from the IEEE Technical Activities Board Committee on Standards (TAB CoS), we were able to explore the attitudes of …

Challenges In Migrating Imperative Deep Learning Programs To Graph Execution: An Empirical Study, Tatiana Castro Vélez, Raffi Khatchadourian, Mehdi Bagherzadeh, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges—and resultant bugs—involved …

Challenges In Migrating Imperative Deep Learning Programs To Graph Execution: An Empirical Study, Tatiana Castro Vélez, Raffi Khatchadourian, Mehdi Bagherzadeh, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges—and resultant bugs—involved …

A Tool For Rejuvenating Feature Logging Levels Via Git Histories And Degree Of Interest, Yiming Tang, Allan Spektor, Raffi Khatchadourian, Mehdi Bagherzadeh

Publications and Research

Logging is a significant programming practice. Due to the highly transactional nature of modern software applications, a massive amount of logs are generated every day, which may overwhelm developers. Logging information overload can be dangerous to software applications. Using log levels, developers can print the useful information while hiding the verbose logs during software runtime. As software evolves, the log levels of logging statements associated with the surrounding software feature implementation may also need to be altered. Maintaining log levels necessitates a significant amount of manual effort. In this paper, we demonstrate an automated approach that can rejuvenate feature log …

Me2b Alliance Validation Testing Report: Consumer Perception Of Legal Policies In Digital Technology, Noreen Y. Whysel, Karina Alexanyan, Shaun Spaulting, Julia Little

Publications and Research

Our relationship with technology involves legal agreements that we either review or enter into when using a technology, namely privacy policies and terms of service or terms of use (“TOS/TOU”). We initiated this research to understand if providing a formal rating of the legal policies (privacy policies and TOS/TOUs) would be valuable to consumers (or Me-s). From our early qualitative discussions, we noticed that people were unclear on whether these policies were legally binding contracts or not. Thus, a secondary objective emerged to quantitatively explore whether people knew who these policies protected (if anyone), and if the policies were perceived …

Messiness: Automating Iot Data Streaming Spatial Analysis, Christopher White, Atilio Barreda Ii

Publications and Research

The spaces we live in go through many transformations over the course of a year, a month, or a day; My room has seen tremendous clutter and pristine order within the span of a few hours. My goal is to discover patterns within my space and formulate an understanding of the changes that occur. This insight will provide actionable direction for maintaining a cleaner environment, as well as provide some information about the optimal times for productivity and energy preservation.

Using a Raspberry Pi, I will set up automated image capture in a room in my home. These images will …

An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja

Publications and Research

Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today’s data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems are prone to classic technical debt issues, especially when such systems are long-lived, but they also exhibit debt specific to these systems. Unfortunately, there is a gap in knowledge of how ML systems evolve and are maintained. In this paper, we fill this gap by studying refactorings, i.e., source-to-source semantics-preserving program transformations, performed in real-world, open-source software, …

An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja

Publications and Research

Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today’s data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems are prone to classic technical debt issues, especially when such systems are long-lived, but they also exhibit debt specific to these systems. Unfortunately, there is a gap of knowledge in how ML systems actually evolve and are maintained. In this paper, we fill this gap by studying refactorings, i.e., source-to-source semantics-preserving program transformations, performed in real-world, open-source …

Shedding Light On Dark Patterns: A Case Study On Digital Harms, Noreen Y. Whysel

Publications and Research

You’ve been there before. You thought you could trust someone with a secret. You thought it would be safe, but found out later that they blabbed to everyone. Or maybe they didn’t share it, but the way they used it felt manipulative. You gave more than you got and it didn’t feel fair. But now that it’s out there, do you even have control anymore?

Ok. Now imagine that person was your supermarket. Or your bank. Or your boss.

As designers of digital spaces for consumer products and services, how often do we consider the relationship we have with our …

Automated Evolution Of Feature Logging Statement Levels Using Git Histories And Degree Of Interest, Yiming Tang, Allan Spektor, Raffi Khatchadourian, Mehdi Bagherzadeh

Publications and Research

Logging—used for system events and security breaches to more informational yet essential aspects of software features—is pervasive. Given the high transactionality of today’s software, logging effectiveness can be reduced by information overload. Log levels help alleviate this problem by correlating a priority to logs that can be later filtered. As software evolves, however, levels of logs documenting surrounding feature implementations may also require modification as features once deemed important may have decreased in urgency and vice-versa. We present an automated approach that assists developers in evolving levels of such (feature) logs. The approach, based on mining Git histories and manipulating …

Actor Concurrency Bugs: A Comprehensive Study On Symptoms, Root Causes, Api Usages, And Differences, Mehdi Bagherzadeh, Nicholas Fireman, Anas Shawesh, Raffi Khatchadourian

Publications and Research

Actor concurrency is becoming increasingly important in the development of real-world software systems. Although actor concurrency may be less susceptible to some multithreaded concurrency bugs, such as low-level data races and deadlocks, it comes with its own bugs that may be different. However, the fundamental characteristics of actor concurrency bugs, including their symptoms, root causes, API usages, examples, and differences when they come from different sources are still largely unknown. Actor software development can significantly benefit from a comprehensive qualitative and quantitative understanding of these characteristics, which is the focus of this work, to foster better API documentations, development practices, …

An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja

Publications and Research

Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today's data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems are prone to classic technical debt issues, especially when such systems are long-lived, but they also exhibit debt specific to these systems. Unfortunately, there is a gap of knowledge in how ML systems actually evolve and are maintained. In this paper, we fill this gap by studying refactorings, i.e., source-to-source semantics-preserving program transformations, performed in real-world, open-source …

An Empirical Study On The Use And Misuse Of Java 8 Streams, Raffi Khatchadourian, Yiming Tang, Mehdi Bagherzadeh, Baishakhi Ray

Publications and Research

Streaming APIs allow for big data processing of native data structures by providing MapReduce-like operations over these structures. However, unlike traditional big data systems, these data structures typically reside in shared memory accessed by multiple cores. Although popular, this emerging hybrid paradigm opens the door to possibly detrimental behavior, such as thread contention and bugs related to non-execution and non-determinism. This study explores the use and misuse of a popular streaming API, namely, Java 8 Streams. The focus is on how developers decide whether or not to run these operations sequentially or in parallel and bugs both specific and tangential …

An Empirical Study On The Use And Misuse Of Java 8 Streams, Raffi Khatchadourian, Yiming Tang, Mehdi Bagherzadeh, Baishakhi Ray

Publications and Research

Streaming APIs allow for big data processing of native data structures by providing MapReduce-like operations over these structures. However, unlike traditional big data systems, these data structures typically reside in shared memory accessed by multiple cores. Although popular, this emerging hybrid paradigm opens the door to possibly detrimental behavior, such as thread contention and bugs related to non-execution and non-determinism. This study explores the use and misuse of a popular streaming API, namely, Java 8 Streams. The focus is on how developers decide whether or not to run these operations sequentially or in parallel and bugs both specific and tangential …

Safe Automated Refactoring For Intelligent Parallelization Of Java 8 Streams, Raffi Khatchadourian, Yiming Tang, Mehdi Bagherzadeh

Publications and Research

Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages and platforms. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite, e.g., collections, and infinite data structures. However, using this API efficiently involves subtle considerations such as determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. ics-preserving fashion. The approach, based on a novel data ordering and typestate analysis, consists of preconditions and …

Going Big: A Large-Scale Study On What Big Data Developers Ask, Mehdi Bagherzadeh, Raffi Khatchadourian

Publications and Research

Software developers are increasingly required to write big data code. However, they find big data software development challenging. To help these developers it is necessary to understand big data topics that they are interested in and the difficulty of finding answers for questions in these topics. In this work, we conduct a large-scale study on Stackoverflow to understand the interest and difficulties of big data developers. To conduct the study, we develop a set of big data tags to extract big data posts from Stackoverflow; use topic modeling to group these posts into big data topics; group similar topics into …

Safe Automated Refactoring For Intelligent Parallelization Of Java 8 Streams, Raffi Khatchadourian, Yiming Tang, Mehdi Bagherzadeh, Syed Ahmed

Publications and Research

Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages and platforms. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite, e.g., collections, and infinite data structures. However, using this API efficiently involves subtle considerations such as determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. Also, streams may not run all operations in parallel depending on particular collectors used in reductions. In …