Linking Code Readability, Structure, and Comprehension Among Novices: It's Complicated

Wiese, Eliane S.; Rafferty, Anna N.; Fox, Armando

doi:10.1109/icse-seet.2019.00017

Cited by 23 publications

(9 citation statements)

References 20 publications

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“…Weise, et al [31], [32] study student preferences for Java and Python code written in different programming styles, and their ability to understand code written in an "expert" style. They find that many students prefer a more naive, or verbose, approach, but are capable of understanding code that uses more expert approaches.…”

Section: Re L a T E D Wo R Kmentioning

confidence: 99%

SQLRepair: Identifying and Repairing Mistakes in Student-Authored SQL Queries

Presler-Marshall

Heckman

Stolee

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)

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Computer science educators seek to understand the types of mistakes that students make when learning a new (programming) language so that they can help students avoid those mistakes in the future. While educators know what mistakes students regularly make in languages such as C and Python, students struggle with SQL and regularly make mistakes when working with it. We present an analysis of mistakes that students made when first working with SQL, classify the types of errors introduced, and provide suggestions on how to avoid them going forward. In addition, we present an automated tool, SQLRepair, that is capable of repairing errors introduced by undergraduate programmers when writing SQL queries. Our results show that students find repairs produced by our tool comparable in understandability to queries written by themselves or by other students, suggesting that SQL repair tools may be useful in an educational context. We also provide to the community a benchmark of SQL queries written by the students in our study that we used for evaluation of SQLRepair.

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Section: Re L a T E D Wo R Kmentioning

confidence: 99%

SQLRepair: Identifying and Repairing Mistakes in Student-Authored SQL Queries

Presler-Marshall

Heckman

Stolee

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)

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“…Such structures were also considered by Wiese et al. [21], where it was accepted that expert opinion is that compound expressions are preferable to nested if statements. But when novices were confronted with such code differences, it did not have a significant effect on their performance, even though they said they found the compound version more readable.…”

Section: Related Workmentioning

confidence: 99%

Does Code Structure Affect Comprehension? On Using and Naming Intermediate Variables

Cates¹,

Yunik²,

Feitelson³

2021

Preprint

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Intermediate variables can be used to break complex expressions into more manageable smaller expressions, which may be easier to understand. But it is unclear when and whether this actually helps. We conducted an experiment in which subjects read 6 mathematical functions and were supposed to give them meaningful names. 113 subjects participated, of which 58% had 3 or more years of programming work experience. Each function had 3 versions: using a compound expression, using intermediate variables with meaningless names, or using intermediate variables with meaningful names. The results were that in only one case there was a significant difference between the two extreme versions, in favor of the one with intermediate variables with meaningful names. This case was the function that was the hardest to understand to begin with. In two additional cases using intermediate variables with meaningless names appears to have caused a slight decrease in understanding. In all other cases the code structure did not make much of a difference. As it is hard to anticipate what others will find difficult to understand, the conclusion is that using intermediate variables is generally desirable. However, this recommendation hinges on giving them good names.

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“…[47]-[55] answer questions about code characteristics 27 studies: [1], [2], [5], [6], [9], [10], [19], [20], [41]- [43], [46], [47], [50], [51], [54]- [65] remember (part of) the code 7 studies: [8], [9], [43], [44], [48], [52], [53] act on the code (15 studies) find and fix bugs in the code 10 studies: [11], [13]- [15], [19], [21], [45], [46], [66], [67] modify the code 8 studies: [10], [13], [45], [46], [56], [62], [66], [67] write code 3 studies: [61], [62], [68] provide personal opinion (30 studies) opinion about the code (readability or legibility)…”

Section: Task Type Studiesmentioning

confidence: 99%

“…23 studies: [3]- [5], [10], [12], [13], [22], [45], [49], [55], [59], [61], [62], [64], [65], [67]- [74] answer if understood the code 4 studies: [42], [55], [59], [60] rate confidence in her answer 3 studies: [8], [20], [53] rate the task difficulty 7 studies: [5], [10], [20], [21], [45], [49], [51] and fix bugs in the code. Scanniello et al [11] asked subjects to do so in two programs with different identifier styles.…”

Section: Task Type Studiesmentioning

confidence: 99%

“…Writing code per se is not a comprehension task, but it may be associated to a comprehension task. For example, Wiese et al [61] first asked subjects to write a function that returns true if the input is 7 and false otherwise so that they could identify what code style (novice, expert, or mixed) the subjects preferred when coding. Afterwards, they asked the subjects to choose the most readable among three versions of a function, each one with a different code style.…”

Section: Task Type Studiesmentioning

confidence: 99%

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Evaluating Code Readability and Legibility: An Examination of Human-centric Studies

Oliveira

Bruno

Madeiral

et al. 2020

2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Reading code is an essential activity in software maintenance and evolution. Several studies with human subjects have investigated how different factors, such as the employed programming constructs and naming conventions, can impact code readability, i.e., what makes a program easier or harder to read and apprehend by developers, and code legibility, i.e., what influences the ease of identifying elements of a program. These studies evaluate readability and legibility by means of different comprehension tasks and response variables. In this paper, we examine these tasks and variables in studies that compare programming constructs, coding idioms, naming conventions, and formatting guidelines, e.g., recursive vs. iterative code. To that end, we have conducted a systematic literature review where we found 54 relevant papers. Most of these studies evaluate code readability and legibility by measuring the correctness of the subjects' results (83.3%) or simply asking their opinions (55.6%). Some studies (16.7%) rely exclusively on the latter variable. There are still few studies that monitor subjects' physical signs, such as brain activation regions (5%). Moreover, our study shows that some variables are multi-faceted. For instance, correctness can be measured as the ability to predict the output of a program, answer questions about its behavior, or recall parts of it. These results make it clear that different evaluation approaches require different competencies from subjects, e.g., tracing the program vs. summarizing its goal vs. memorizing its text. To assist researchers in the design of new studies and improve our comprehension of existing ones, we model program comprehension as a learning activity by adapting a preexisting learning taxonomy. This adaptation indicates that some competencies, e.g., tracing, are often exercised in these evaluations whereas others, e.g., relating similar code snippets, are rarely targeted.

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Linking Code Readability, Structure, and Comprehension Among Novices: It's Complicated

Cited by 23 publications

References 20 publications

SQLRepair: Identifying and Repairing Mistakes in Student-Authored SQL Queries

SQLRepair: Identifying and Repairing Mistakes in Student-Authored SQL Queries

Does Code Structure Affect Comprehension? On Using and Naming Intermediate Variables

Evaluating Code Readability and Legibility: An Examination of Human-centric Studies

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