Understanding semantic style by analysing student code

Ruvo, Giuseppe De; Tempero, Ewan; Luxton-Reilly, Andrew; Rowe, Gerard; Giacaman, Nasser

doi:10.1145/3160489.3160500

Cited by 33 publications

(29 citation statements)

References 31 publications

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“…Several studies investigated non-functional problems in student code (e.g. [9,10,17]), from which we learn they are evident and often remain unfixed. Studies show mixed results regarding whether students address issues, which is apparently more the case when they are being assessed on it.…”

Section: Background and Related Work 21 Code Quality In Educationmentioning

confidence: 99%

“…The hints the system gives are based on rules, derived from teacher suggestions for a set of imperfect student programs, collected in our earlier study that investigated how teachers would give feedback on improving code [18]. Other rules are based on other studies [9,36], a subset of rules from professional static analysis tools considered suitable for novices, and equality rules from arithmetic and logic. The system also contains some 'buggy rules' that describe common mistakes.…”

Section: The Refactor Tutormentioning

confidence: 99%

“…Studies have found numerous qualitative issues in student code [9,10,17]. If we assess students solely based on functional behaviour through test cases, they might not see the importance of writing high-quality code.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Student Refactoring Behaviour in a Programming Tutor

Keuning

Heeren

Jeuring

2020

Koli Calling '20: Proceedings of the 20th Koli Calling International Conference on Computing Education Research

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Section: Background and Related Work 21 Code Quality In Educationmentioning

confidence: 99%

Section: The Refactor Tutormentioning

confidence: 99%

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Student Refactoring Behaviour in a Programming Tutor

Keuning

Heeren

Jeuring

2020

Koli Calling '20: Proceedings of the 20th Koli Calling International Conference on Computing Education Research

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“…Breuker et al [6] found no clear quality improvement between the code of first-and second-year students. De Ruvo et al [8] investigated a set of 19.000 code submissions on 16 semantic style indicators, which address small issues such as unnecessary return statements, and too complex if-statements. They found instances in both code of novices and more experienced students.…”

Section: Code Quality In Educationmentioning

confidence: 99%

“…An increasing number of studies have focused on the quality of programs written by novices, as opposed to the correctness of student programs, which has had quite some attention in research the past decades [7,16]. These studies show that student programs contain a substantial amount of various quality issues, which often remain unsolved [8,11,15]. While there has not been much research into the reasons why quality issues remain unsolved, one can imagine that students are satisfied once their solutions pass all tests.…”

Section: Introductionmentioning

confidence: 99%

How Teachers Would Help Students to Improve Their Code

Keuning

Heeren

Jeuring

2019

Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education

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Code quality has been receiving less attention than program correctness in both the practice of and research into programming education. Writing poor quality code might be a sign of carelessness, or not fully understanding programming concepts and language constructs. Teachers play an important role in addressing quality issues, and encouraging students to write better code as early as possible.In this paper we explore to what extent teachers address code quality in their teaching, which code quality issues they observe and how they would help novices to improve their code. We presented student code of low quality to 30 experienced teachers and asked them which hints they would give and how the student should improve the code step by step. We compare these hints to the output of professional code quality tools.Although most teachers gave similar hints on reducing the algorithmic complexity and removing clutter, they gave varying subsets of hints on other topics. We found a large variety in how they would solve issues in code. We noticed that professional code quality tools do not point out the algorithmic complexity topics that teachers mention. Finally, we give some general guidelines on how to approach code improvement.

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