An Empirical Investigation into Programming Language Syntax
By Andreas Stefik, Susanna Siebert. TOCE, 2013
298 citations recorded by the source corpus.
Subfields: Computer Science Applications, Information Systems, Software
View Paper: https://www.semanticscholar.org/paper/215ac9b23a9a89ad7c8f22b5f9a9ad737204d820
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Science Applications
- Paul Denny
- Juho Leinonen
- Sami Sarsa
- Arto Hellas
- Andrew Luxton-Reilly
- James Prather
- Brett A. Becker
- Claudia Szabo
In Information Systems
- Paul Denny
- Juho Leinonen
- Sami Sarsa
- Arto Hellas
- Andrew Luxton-Reilly
- James Prather
- Claudia Szabo
- David Bau
In Software
- Paul Denny
- Juho Leinonen
- Arto Hellas
- Sami Sarsa
- Claudia Szabo
- Andrew Luxton-Reilly
- James H. Paterson
Citations in the corpus
- Using Large Language Models to Enhance Programming Error Messages (2022, background): “Instructors report that they spend a considerable amount of time helping novices with these often cryptic messages [14, 29, 30, 34].”
- Learnable programming (2017, background): “ all levels can benet from working online. 3.4.2 Words, Concepts, and Abstractions The names chosen for language constructs can have an impact on learnability. Empirical studies by Stek and Seibert [34] have found that common keywords like for or operators such as != serve as hard-to-learn jargon, a”
- Introductory programming: a systematic literature review (2018, background): “While all of the language-based papers mentioned above have the potential to guide educators in their own choice of language, a number of authors make that guidance the focus of their papers. de Raadt et al. [152] discuss the industry and academic implications of the choice of language; Parker et al”