The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Empirical Studies of Programming Knowledge
Abstract
We suggest that expert programmers have and use two types of programming knowledge: 1) programming plans, which are generic program fragments that represent stereotypic action sequences in programming, and 2) rules of programming discourse, which capture the conventions in programming and govern the composition of the plans into programs. We report here on two empirical studies that attempt to evaluate the above hypothesis. Results from these studies do in fact support our claim.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Chase, W. G., & Simon, H. A. (1973) · Cognitive Psychology · doi:10.1016/0010-0285(73)90004-2 · Cited by 4,479
Chunking in recall of symbolic drawings
Egan, D. E., & Schwartz, B. J. (1979) · Memory & Cognition · doi:10.3758/bf03197595 · Cited by 482
Cited by
Learning and Teaching Programming: A Review and Discussion
Robins, A., Rountree, J., & Rountree, N. (2003) · Computer Science Education · doi:10.1076/csed.13.2.137.14200 · Cited by 1,736
On the fragility of skilled performance: What governs choking under pressure?
Beilock, S. L., & Carr, T. H. (2001) · Journal of Experimental Psychology: General · doi:10.1037/0096-3445.130.4.701 · Cited by 1,201
Some Difficulties of Learning to Program
Du Boulay, B. (1986) · Journal of Educational Computing Research · doi:10.2190/3lfx-9rrf-67t8-uvk9 · Cited by 648
A multi-national study of reading and tracing skills in novice programmers
Lister, R., Adams, E. S., Fitzgerald, S., Fone, W., Hamer, J., Lindholm, M., McCartney, R., Moström, J. E., Sanders, K., Seppälä, O., et al. (2004) · ACM SIGCSE Bulletin · doi:10.1145/1041624.1041673 · Cited by 575
Stimulus structures and mental representations in expert comprehension of computer programs
Pennington, N. (1987) · Cognitive Psychology · doi:10.1016/0010-0285(87)90007-7 · Cited by 526
Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models
Sarsa, S., Denny, P., Hellas, A., & Leinonen, J. (2022) · Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 1 · doi:10.1145/3501385.3543957 · Cited by 497
Questions programmers ask during software evolution tasks
Sillito, J., Murphy, G. C., & De Volder, K. (2006) · Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering · doi:10.1145/1181775.1181779 · Cited by 270
Conditions of Learning in Novice Programmers
Perkins, D. N., Hancock, C., Hobbs, R., Martin, F., & Simmons, R. (1986) · Journal of Educational Computing Research · doi:10.2190/gujt-jcbj-q6qu-q9pl · Cited by 262
Understanding understanding source code with functional magnetic resonance imaging
Siegmund, J., Kästner, C., Apel, S., Parnin, C., Bethmann, A., Leich, T., Saake, G., & Brechmann, A. (2014) · Proceedings of the 36th International Conference on Software Engineering · doi:10.1145/2568225.2568252 · Cited by 219
A theory of instruction for introductory programming skills
Xie, B., Loksa, D., Nelson, G. L., Davidson, M. J., Dong, D., Kwik, H., Tan, A. H., Hwa, L., Li, M., & Ko, A. J. (2019) · Computer Science Education · doi:10.1080/08993408.2019.1565235 · Cited by 191
On the Comprehension of Program Comprehension
Maalej, W., Tiarks, R., Roehm, T., & Koschke, R. (2014) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/2622669 · Cited by 180
(2017) · doi:10.1145/3106237.3106268 · Cited by 117
Towards understanding programs through wear-based filtering
DeLine, R., Khella, A., Czerwinski, M., & Robertson, G. (2005) · Proceedings of the 2005 ACM symposium on Software visualization · doi:10.1145/1056018.1056044 · Cited by 91
Liu, Y.-F., Kim, J., Wilson, C., & Bedny, M. (2020) · eLife · doi:10.7554/elife.59340 · Cited by 64
Spreadsheet Comprehension: Guesswork, Giving Up and Going Back to the Author
Srinivasa Ragavan, S., Sarkar, A., & Gordon, A. D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445634 · Cited by 67
Peitek, N., Bergum, A., Rekrut, M., Mucke, J., Nadig, M., Parnin, C., Siegmund, J., & Apel, S. (2022) · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3540250.3549084 · Cited by 32
Supporting skill integration in an intelligent tutoring system for code tracing
Huang, Y., Brusilovsky, P., Guerra, J., Koedinger, K., & Schunn, C. (2023) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12757 · Cited by 32
A Theory of Robust API Knowledge
Thayer, K., Chasins, S. E., & Ko, A. J. (2021) · ACM Transactions on Computing Education · doi:10.1145/3444945 · Cited by 25
Typing Exercises as Interactive Worked Examples for Deliberate Practice in CS Courses
Gaweda, A. M., Lynch, C. F., Seamon, N., Silva de Oliveira, G., & Deliwa, A. (2020) · Proceedings of the Twenty-Second Australasian Computing Education Conference · doi:10.1145/3373165.3373177 · Cited by 21
Computing Education Research Today
Fincher, S. A., Tenenberg, J., Dorn, B., Hundhausen, C., McCartney, R., & Murphy, L. (2019) · The Cambridge Handbook of Computing Education Research · doi:10.1017/9781108654555.003 · Cited by 7
Code Review Comprehension: Reviewing Strategies Seen Through Code Comprehension Theories
Gonçalves, P. W., Rani, P., Storey, M.-A., Spinellis, D., & Bacchelli, A. (2025) · 2025 IEEE/ACM 33rd International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc66645.2025.00068 · Cited by 4
Metadata
- Authors
- Soloway, E., & Ehrlich, K.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 1984-09
- Volume
- SE-10
- Issue
- 5
- Pages
- 595-609
- ISSN
- 0098-5589 (ISSN-L)
- OpenAlex
- W2133247167
- Cited by
- 877
- References
- 2
- FWCI
- 78.10
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.