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1. A Software Debugger for E-textiles and Arduino Microcontrollers

   Schneider, M.; Hill, C.; Eisenberg, A.; Gross, M.; Blum, A. (October 2020, FabLearn 2020 - 9th Annual Conference on Maker Education (FabLearn ’20))

   Today’s STEM classrooms have expanded the domain of computer science education from a basic two-toned terminal screen to now include helpful Integrated Development Environments(IDE) (BlueJ, Eclipse), block-based programming (MIT Scratch, Greenfoot), and even physical computing with embedded systems (Arduino, LEGO Mindstorm). But no matter which environment a student starts programming in, all students will eventually need help in finding and fixing bugs in their code. While the helpful IDE’s have debugger tools built in (breakpoints for pausing your program, ways to view/modify variable values, and "stepping" through code execution), in many of the other programming environments, students are limited to using print statements to try and "see" what is happening inside their program. Most students who learn to write code for Arduino microcontrollers will start within the Arduino IDE, but the official Arduino IDE does not currently provide any debugging tools. Instead, a student would have to move on to a professional IDE such as Atmel Studio or acquire a hardware debugger in order to add breakpoints or view their program’s variables. But each of these options has a steep learning curve, additional costs, and can require complex configurations. Based on research of student debugging practices[3, 7] and our own classroom observations, we have developed an Arduino software library, called Arduino Debugger, which provides some of these debugging tools (ex. breakpoints) while staying within the official Arduino IDE. This work continues a previous library, (redacted), which focused on features specific to e-textiles development boards. The Arduino Debugger library has been modified to support not only e-textile boards (Lilypad, Adafruit Circuit Playground) but most AVR and ARM based Arduino boards.We are also in the process of testing a set of Debugging Code Templates to see how they might increase student adoption of debugging tools. 

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2. Evaluating How Novices Utilize Debuggers and Code Execution to Understand Code

   https://doi.org/10.1145/3632620.3671126  

   Hassan, Mohammed; Zeng, Grace; Zilles, Craig (August 2024, ACM)

   Background: Previous work has shown that students can understand more complicated pieces of code through the use of common software development tools (code execution, debuggers) than they can without them. Objectives: Given that tools can enable novice programmers to understand more complex code, we believe that students should be explicitly taught to do so, to facilitate their plan acquisition and development as independent programmers. In order to do so, this paper seeks to understand: (1) the relative utility of these tools, (2) the thought process students use to choose a tool, and (3) the degree to which students can choose an appropriate tool to understand a given piece of code. Method: We used a mixed-methods approach. To explore the relative effectiveness of the tools, we used a randomized control trial study (𝑁 = 421) to observe student performance with each tool in understanding a range of different code snippets. To explore tool selection, we used a series of think-aloud interviews (𝑁 = 18) where students were presented with a range of code snippets to understand and were allowed to choose which tool they wanted to use. Findings: Overall, novices were more often successful comprehending code when provided with access to code execution, perhaps because it was easier to test a larger set of inputs than the debugger. As code complexity increased (as indicated by cyclomatic complexity), students become more successful with the debugger. We found that novices preferred code execution for simpler or familiar code, to quickly verify their understanding and used the debugger on more complex or unfamiliar code or when they were confused about a small subset of the code. High-performing novices were adept at switching between tools, alternating from a detail-oriented to a broader perspective of the code and vice versa, when necessary. Novices who were unsuccessful tended to be overconfident in their incorrect understanding or did not display a willingness to double check their answers using a debugger. Implications: We can likely teach novices to independently understand code they do not recognize by utilizing code execution and debuggers. Instructors should teach students to recognize when code is complex (e.g., large number of nested loops present), and to carefully step through these loops using debuggers. We should additionally teach students to be cautious to double check their understanding of the code and to self-assess whether they are familiar with the code. They can also be encouraged to strategically switch between execution and debuggers to manage cognitive load, thus maximizing their problem-solving capabilities. 

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3. Towards Safe Automated Refactoring of Imperative Deep Learning Programs to Graph Execution

   https://doi.org/10.1109/ASE56229.2023.00187  

   Khatchadourian, Raffi; Castro-Vélez, Tatiana; Bagherzadeh, Mehdi; Jia, Nan; Raja, Anita (September 2023, IEEE/ACM International Conference on Automated Software Engineering)

   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 and how their otherwise eagerly-executed imperative DL code could be reliably and efficiently executed as graphs while preserving semantics. The approach, based on a novel imperative tensor analysis, will automatically determine when it is safe and potentially advantageous to migrate imperative DL code to graph execution and modify decorator parameters or eagerly executing code already running as graphs. The approach is being implemented as a PyDev Eclipse IDE plug-in and uses the WALA Ariadne analysis framework. We discuss our ongoing work towards optimizing imperative DL code to its full potential. 

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4. Program Comprehension with Physical Computing: A Structure, Function, and Behavior Analysis of Think-Alouds with High School Students.

   https://doi.org/10.1145/3430665.3456371  

   Jayathirtha, G.; Kafai, Y. B. (January 2021, Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education)

   Comprehending programs is key to learning programming. Previous studies highlight novices’ naive approaches to comprehend ing the structural, functional, and behavioral aspects of programs. And yet, with the majority of them examining on-screen program ming environments, we barely know about program comprehension within physical computing—a common K-12 programming context. In this study, we qualitatively analyzed think-aloud inter view videos of 22 high school students individually comprehending a given text-based Arduino program while interacting with its corresponding functional physical artifact to answer two questions: 1) How do novices comprehend the given text-based Arduino pro gram? And, 2) What role does the physical artifact play in program comprehension? We found that novices mostly approached the program bottom-up, initially comprehending structural and later functional aspects, along different granularities. The artifact provided two distinct modes of engagement, active and interactive, that supported the program’s structural and functional comprehension. However, behavioral comprehension i.e. understanding program execution leading to the observed outcome was inaccessible to many. Our findings extend program comprehension literature in two ways: (a) it provides one of the very few accounts of high school students’ code comprehension in a physical computing con text, and, (b) it highlights the mediating role of physical artifacts in program comprehension. Further, they point directions for future pedagogical and tool designs within physical computing to better support students’ distributed program comprehension. 

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5. Objects to debug with: How young children resolve errors with tangible coding toys

   Silvis, D.; Clarke-Midura, J.; Shumway, J.; & Lee, V. R. (January 2021, Proceedings of the International Society of the Learning Sciences (ISLS) Annual Meeting)

   Debugging is an important skill all programmers must learn, including preliterate children who are learning to code in early childhood settings. Despite the fact that early learning environments increasingly incorporate coding curricula, we know little about debugging knowledge in early childhood. One reason is that the tangible programming environments designed for young children entail a layer of material complexity that we have yet to account for in terms of learning to debug. In our study of young children learning to program, we found that in the midst of solving programming tasks and learning to debug, tangible toys presented bugs of their own. This paper analyzes video of Kindergarteners learning to debug errors in the program and errors in the physical materials. We argue that concurrent physical and programming bugs present opportunities for young children to learn about the broader computational system in which they are learning to code. 

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