Meaningfulness as a factor of program complexity

Chaudhary, B.D.; Sahasrabuddhe, H.V.

doi:10.1145/800176.810001

Cited by 8 publications

(7 citation statements)

References 12 publications

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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%

“…Present Explain a solution to others. Read a program and then write a description of what it does and how [48]. Recognize Base knowledge, vocabulary of the domain.…”

Section: Modelmentioning

confidence: 99%

“…Finally, there are some nonintuitive relationships between tasks and activities. Chaudhary [48] asked the subjects to reconstruct a program after spending some time looking at it. This is a "Remember the code" task where the subjects have to explain what the program does by writing a similar program.…”

Section: B Mapping Tasks To Learning Activitiesmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Task Type Studiesmentioning

confidence: 99%

“…Present Explain a solution to others. Read a program and then write a description of what it does and how [48]. Recognize Base knowledge, vocabulary of the domain.…”

Section: Modelmentioning

confidence: 99%

See 1 more Smart Citation

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)

View full text Add to dashboard Cite

show abstract

“…Also there is no penalty for embedded loops and a series of single loops. Chaudhary and Shastrabuddhe [5] discuss about the study of the effect of meaningfulness of a program and its understanding and consider difficulty or psychological complexity of program. Hamer [6] describes the test made for the validity of the relationships and interpretations that form the foundation of software science.…”

Section: Literature Review For Estimation Of Software Complexitymentioning

confidence: 99%

An empirical approach for early estimation of software testing effort using SRS document

Sharma

Kushwaha

2012

CSIT

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“…In the past 40 years, researchers in the software engineering community have been studying various factors that effect program comprehension, including the source code lexicon, comments, and readability [5,8,9,25,26,47,49,57]. Studies have shown that roughly half of the cost of software maintenance stems from activities related to program comprehension [13], and developers spend the majority of their time understanding source code [48,50,53,58].…”

Section: Introductionmentioning

confidence: 99%

A Model to Detect Readability Improvements in Incremental Changes

Roy

Fakhoury

Lee

et al. 2020

Proceedings of the 28th International Conference on Program Comprehension

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Identifying source code that has poor readability allows developers to focus maintenance efforts on problematic code. Therefore, the effort to develop models that can quantify the readability of a piece of source code has been an area of interest for software engineering researchers for several years. However, recent research questions the usefulness of these readability models in practice. When applying these models to readability improvements that are made in practice, i.e., commits, they are unable to capture these incremental improvements, despite a clear perceived improvement by the developers. This results in a discrepancy between the models we have built to measure readability, and the actual perception of readability in practice. In this work, we propose a model that is able to detect incremental readability improvements made by developers in practice with an average precision of 79.2% and an average recall of 67% on an unseen test set. We then investigate the metrics that our model associates with developer perceived readability improvements as well as non-readability changes. Finally, we compare our model to existing state-of-the-art readability models, which our model outperforms by at least 23% in terms of precision and 42% in terms of recall. CCS CONCEPTS • Software and its engineering → Maintaining software; • Computing methodologies → Machine learning.

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Meaningfulness as a factor of program complexity

Cited by 8 publications

References 12 publications

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

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

An empirical approach for early estimation of software testing effort using SRS document

A Model to Detect Readability Improvements in Incremental Changes

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