Lectures and Details

Overview

• Does your brain treat writing a program more like writing a poem or more like solving an equation?
• What does the psychology of perception say about user interface design?
• Does balancing a pen on your finger show the same patterns of neural activity as balancing a tree data structure?
• How do cognitive biases influence code review?
• What do professional programmers think about the benefits and drawbacks of substances like Adderall or cannabis?
• What does the psychology of trust teach us about human decisions to deploy tools?

This seminar course covers the intersection of software topics (e.g., reading, writing and debugging programs) and human factors (e.g., perception, bias, cognition). It will involve an exploration of classic topics in psychology in a manner that is approachable to students without prior psychology experience. We will relate these topics to activities in computer science and we will discuss the resulting implications on both academic and industrial practice. Students will be graded on in-class discussions and short presentations on the material. Group discussions will focus on constructive conversations that summarize the work, analyze its strengths, and brainstorm ways in which it might be improved in the future.

Special emphasis will be placed on instructor-led in-person discussions of contextual aspects of papers that may be less evident to students (e.g., based on the instructor's first-hand knowledge of the authors or research situation). These may include:

• The impact or influence the paper had on subsequent work
• Whether some aspects of the paper were accidental or planned
• Why certain reasonable-seeming approaches were not taken
• How difficult certain types of the work were to carry out

Such discussions can help students to view papers as part of a collaborative, human activity takes place over a span of time. Conversations about how to construct well-designed follow-on work (e.g., balancing risk and rewarding, lifting input assumptions, etc.), at the level of the graduate Preliminary Exam, will be encouraged.

Software and Cognition

This class will include papers that explicitly cover human cognitive aspects of software engineering and programming languages, such as studies that make use of eye tracking or medical imaging.

It is not expected that incoming students have any expertise in such cognitive aspects. Instead, relevant cognitive aspects will be discussed (including in the third paper, below) such that students can interpret their relevance to computer science.

Structure and Presentations

Each selected student will give a five-minute in-person presentation that at least (1) summarizes the work, (2) lists its strengths, and (3) lists ways in which it might be improved in a subsequent publication. Including other information, such as your opinion about the work or its relation to other projects, is recommended.

The goals of this randomized approach are to encourage all participants to read the material thoroughly in advance, to provide jumping-off points for detailed discussions, and to allow me to evaluate participation.

There is no project component to this course — your primary responsibility is to prepare five minutes of cogent discussion for each paper.

Grading Rubric

Grading will be based on in-person participation.

In more detail, Paper Presentations will be graded on the summarization of the work (20%), the enumeration of strengths (30%), and areas and mechanisms for improvement (50%).

• While paper summarizations are common in many settings, this course places a particular emphasis on identifying areas for improvement that may not be mentioned by the authors of a work. For example, a paper may present an analysis that only works on single-threaded programs, but the paper may not call out this weakness explicitly. Instead, readers may note that all of the benchmarks were single-threaded and that the analysis relies on the values of variables remaining constant. Readers might identify this area for improvement and also identify a potential mechanism for overcoming it (e.g., perhaps the analysis could be augmented with manual annotations about which variables are modified by other threads, or perhaps a dynamic lockset approach like Eraser could be used to provide missing information, etc.).
• This identification of paper areas for improvement that are not explicit but are instead implicit (in the "negative space" of the paper, as it were) is an important skill, and one that we will practice together. Similarly, we will work together on identifying how to bring together topics and ideas from computer science to address such weaknesses.

The Discussion component will be assessed by noting when students contribute to the conversation and analysis of a paper (outside of their presentations).

• Student contributions will be noted on a three-point scale, with two points for a typical discussion element, three points for a more insightful analysis, and one point for a less fruitful interjection.
• Discussion points are graded on insight and relevant, rather than on length. A theme of the course is that sometimes a single sentence or observation can significantly influence how a paper is interpreted. The focus is not on long-winded reiterations of the main paper points, but on identifying "interactions" (e.g., "given that they mention in Section 3 that they are recruiting only students, their use of this industrial task in Section 5 may not be indicative ...", etc.) or areas for improvement.
• Particularly insightful contributions will be described as such by the instructor (e.g., to help students who may not be as familiar with this sort of course to identify the desired approach).
• Each student can earn at most three points per class meeting. This is to prevent one student from dominating a discussion while also encouraging students to speak multiple times per meeting.
• If there are N course meetings, full discussion credit corresponds to N earned points, distributed in any manner.
• Students can make up the discussion component in Office Hours, earning up to three points per session.
• The relative simplicity of discussion grading (informally: check-plus, check, check-minus) also helps the professor track it without interrupting the flow of the conversation.

The Professionalism component will be assessed in terms of helping to maintain a welcoming environment for everyone and demonstrating our shared values. Participants are assumed to be professionals by default, but may lose points in certain negative circumstances. Examples of less-professional conduct include:

• Rudely interrupting or speaking over someone else. Instead, we favor giving speakers time to complete their thoughts. (The professor will gently move along any stalled or overly-verbose discussion.)
• Suggesting that the quality of a particular paper or statement is in any way related to the demographics of its authors. Instead, we assess papers on the merits of their presented ideas and evidence.
• Intentionally using defamatory or belittling phrasing to describe people or groups. Some of the papers we will read and discuss tackle practitioner biases or expertise differences. We want to factually discuss such biases, with a goal of mitigating, rather than perpetuating, those concerns.

Informally, one sufficient condition for an "A" grade is to do a good job whenever you are called on to present a paper, to interject insightful comments into the discussions of other papers, and to treat others respectfully.

The grading cutoffs are:

• 70% = C-
• 73% = C
• 77% = C+
• 80% = B-
• 83% = B
• 87% = B+
• 90% = A-
• 93% = A
• 98% = A+

Missed Meetings

Students who miss a meeting for College-recognized reasons and who are selected to present that day can make up the presentation assessment in Office Hours (preferred) or via Zoom (potentially). In such a case you may be asked to present your summary of any recent paper (not necessarily just the one discussed while you were away) and your summary may be held to a slightly higher standard (but not that much higher) because you definitively know the presentation is coming. That additional assessment will likely take the form of a more back-and-forth conversation about the paper, similar to the discussions in class but limited to you and the professor.

First Class Meeting

The first meeting will take place on Tuesday, January 23rd. You are responsible for reading the first two papers in advance.

However, I will get the discussion rolling for Paper #1 ("Producing wrong data ..."), so you do not need to prepare a presentation for it. The discussion for it will probably be short.

I will begin randomly calling on students when we advance to Paper #2. You should prepare your five minute discussions for that paper.

Reading List

On average, we will discuss three papers a week (devoting perhaps 50 minutes to each paper). However, some papers may merit more or less discussion.

It is often possible to find presentation slides or video recordings associated with a paper. You are welcome to use those as part of your preparation.

The next paper to discuss may be shown below a highlighted line. You should read and prepare at least the next two-to-three papers before the next meeting.

Software Research and Threats to Validity

1. Todd Mytkowicz, Amer Diwan, Matthias Hauswirth, Peter F. Sweeney: Producing wrong data without doing anything obviously wrong! ASPLOS 2009: 265-276
2. Janet Siegmund, Norbert Siegmund, Sven Apel: Views on Internal and External Validity in Empirical Software Engineering. ICSE 2015: 9-19 (distinguished paper award)

Neuroscience and Language

3. Jingyuan E. Chen, Gary H. Glover: Functional Magnetic Resonance Imaging Methods. Neuropsychology Review, vol. 25, pages 289-313 (2015)
4. Ioulia Kovelman, Stephanie A. Baker, Laura-Ann Petitto: Bilingual and Monolingual Brains Compared: A Functional Magnetic Resonance Imaging Investigation of Syntactic Processing and a Possible "Neural Signature" of Bilingualism. J. Cogn. Neurosci. 20(1): 153-169 (2008)

Software Engineering and the Brain

5. Janet Siegmund, Christian Kästner, Sven Apel, Chris Parnin, Anja Bethmann, Thomas Leich, Gunter Saake, André Brechmann: Understanding understanding source code with functional magnetic resonance imaging. ICSE 2014: 378-389
6. Benjamin Floyd, Tyler Santander, Westley Weimer: Decoding the representation of code in the brain: an fMRI study of code review and expertise. ICSE 2017: 175-186 (distinguished paper award)
7. Yu Huang, Xinyu Liu, Ryan Krueger, Tyler Santander, Xiaosu Hu, Kevin Leach, Westley Weimer: Distilling neural representations of data structure manipulation using fMRI and fNIRS. ICSE 2019: 396-407 (distinguished paper award)
8. Ryan Krueger, Yu Huang, Xinyu Liu, Tyler Santander, Westley Weimer, Kevin Leach: Neurological Divide: An fMRI Study of Prose and Code Writing. International Conference on Software Engineering (ICSE): 2020
9. Zachary Karas, Andrew Jahn, Westley Weimer, Yu Huang: Connecting the Dots: Rethinking the Relationship between Code and Prose Writing with Functional Connectivity: Foundations of Software Engineering (ESEC/FSE): 2021

Software Expertise and the Brain

10. Norman Peitek, Annabelle Bergum, Maurice Rekrut, Jonas Mucke, Matthias Nadig, Chris Parnin, Janet Siegmund, Sven Apel: Correlates of programmer efficacy and their link to experience: a combined EEG and eye-tracking study. ESEC/SIGSOFT FSE 2022: 120-131
11. Ikutani Y, Kubo T, Nishida S, Hata H, Matsumoto K, Ikeda K, Nishimoto S. Expert Programmers Have Fine-Tuned Cortical Representations of Source Code. eNeuro. 2021 Jan 28. 8(1): ENEURO.0405-20.2020.
12. Hammad Ahmad, Madeline Endres, Kaia Newman, Priscila Santiesteban, Emma Shedden, Westley Weimer: Causal Relationships and Programming Outcomes: A Transcranial Magnetic Stimulation Experiment: International Conference on Software Engineering (ICSE) 2024

Psychoactive Substances and Neurodiversity

13. Madeline Endres, Kevin Boehnke, Westley Weimer: Hashing It Out: A Survey of Programmers' Cannabis Usage, Perception, and Motivation: International Conference on Software Engineering (ICSE) (2022)
14. Kaia Newman, Madeline Endres, Westley Weimer, Brittany Johnson: From Organizations to Individuals: Psychoactive Substance Use By Professional Programmers: International Conference on Software Engineering (ICSE) 2023
15. Meredith Ringel Morris, Andrew Begel, Ben Wiedermann: Understanding the Challenges Faced by Neurodiverse Software Engineering Employees: Towards a More Inclusive and Productive Technical Workforce. ASSETS 2015: 173-184
16. Wenxin He, Manasvi Parikh, Westley Weimer, Madeline Endres: High Expectations: An Observational Study of Programming and Cannabis Intoxication: International Conference on Software Engineering (ICSE) 2024

Implications — Code Comprehension

17. R. W. Proctor, D. W. Schneider: Hick's law for choice reaction time: A review. Quarterly Journal of Experimental Psychology, 2018, 71(6), 1281-1299.
18. Sarah Fakhoury, Devjeet Roy, Yuzhan Ma, Venera Arnaoudova, Olusola O. Adesope: Measuring the impact of lexical and structural inconsistencies on developers' cognitive load during bug localization. Empir. Softw. Eng. 25(3): 2140-2178 (2020)
19. Norman Peitek, Sven Apel, Chris Parnin, André Brechmann, Janet Siegmund: Program Comprehension and Code Complexity Metrics: An fMRI Study. ICSE 2021: 524-536 (distinguished paper award)

Guest Speaker

20. Visit by Kaia Newman

Implications — Code Review and Trust

21. Tyler J. Ryan, Gene M. Alarcon, Charles Walter, Rose F. Gamble, Sarah A. Jessup, August A. Capiola, Marc D. Pfahler: Trust in Automated Software Repair - The Effects of Repair Source, Transparency, and Programmer Experience on Perceived Trustworthiness and Trust. HCI (29) 2019: 452-470
22. Yu Huang, Kevin Leach, Zohreh Sharafi, Nicholas McKay, Tyler Santander, Westley Weimer: Biases and Differences in Code Review using Medical Imaging and Eye-Tracking: Genders, Humans, and Machines: Foundations of Software Engineering (ESEC/FSE): 2020

Guest Speaker

23. Visit by Madeline Endres

Implications — Learning and Teaching

24. Ryan Shaun Joazeiro de Baker, Sidney K. D'Mello, Ma. Mercedes T. Rodrigo, Arthur C. Graesser: Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments. Int. J. Hum. Comput. Stud. 68(4): 223-241 (2010)
25. Naser Al Madi, Cole S. Peterson, Bonita Sharif, Jonathan I. Maletic: From Novice to Expert: Analysis of Token Level Effects in a Longitudinal Eye Tracking Study. ICPC 2021: 172-183
26. Hammad Ahmad, Zachary Karas, Kimberly Diaz, Amir Kamil, Jean-Baptiste Jeannin, Westley Weimer: How Do We Read Formal Claims? Eye-Tracking and the Cognition of Proofs about Algorithms. ICSE 2023: 208-220
27. Nischal Shrestha, Colton Botta, Titus Barik, Chris Parnin: Here we go again: why is it difficult for developers to learn another programming language? Commun. ACM 65(3): 91-99 (2022) (distinguished paper award)
28. Madeline Endres, Zachary Karas, Xiaosu Hu, Ioulia Kovelman, Westley Weimer: Relating Reading, Visualization, and Coding for New Programmers: A Neuroimaging Study: International Conference on Software Engineering (ICSE): (2021)
29. Madeline Endres, Madison Fansher, Priti Shah, Westley Weimer: To Read or To Rotate? Comparing the Effects of Technical Reading Training and Spatial Skills Training on Novice Programming Ability: Foundations of Software Engineering (ESEC/FSE): 2021
30. Chris Parnin, Alessandro Orso: Are automated debugging techniques actually helping programmers? ISSTA 2011: 199-209
31. E. Soremekun, L. Kirschner, M. Böhme and M. Papadakis: Evaluating the Impact of Experimental Assumptions in Automated Fault Localization: International Conference on Software Engineering (ICSE), 2023, pp. 159-171, doi: 10.1109/ICSE48619.2023.00025.

Software Engineering and Deep Learning

32. Ru Zhang, Wencong Xiao, Hongyu Zhang, Yu Liu, Haoxiang Lin, Mao Yang: An empirical study on program failures of deep learning jobs. ICSE 2020: 1159-1170 (distinguished paper award)
33. Hung Viet Pham, Shangshu Qian, Jiannan Wang, Thibaud Lutellier, Jonathan Rosenthal, Lin Tan, Yaoliang Yu, Nachiappan Nagappan: Problems and Opportunities in Training Deep Learning Software Systems: An Analysis of Variance. ASE 2020: 771-783 (distinguished paper award)

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34. No class 4/30 (instructor illness)

Recent Results

35. Yimeng Ma, Yu Huang, Kevin Leach: Breaking the Flow: A Study of Interruptions During Software Engineering Activities ICSE 2024 (distinguished paper award)
36. Paloma Guenes, Rafael Tomaz, Marcos Kalinowski, Maria Teresa Baldassarre, Margaret-Anne Storey: Impostor Phenomenon in Software Engineers ICSE — Software Engineering in Society 2024
37. Grischa Liebel, Noah Langlois, Kiev Gama: Challenges, Strengths, and Strategies of Software Engineers with ADHD: A Case Study ICSE — Software Engineering in Society 2024