How does code readability change during software evolution?

Piantadosi, Valentina; Fierro, Fabiana; Scalabrino, Simone; Serebrenik, Alexander; Oliveto, Rocco

doi:10.1007/s10664-020-09886-9

Cited by 15 publications

(5 citation statements)

References 51 publications

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“…Similarly, a study on Eclipse interface usage by Eclipse third-party plug-ins found that a significant portion of Eclipse third-party plug-ins uses "bad" interfaces and the bad usage was not removed from the systems (Businge et al 2015). This phenomenon is further discovered by the study on how code readability changes during software evolution (Piantadosi et al 2020). The result shows that unreadable code is a minority and most of the unreadable pieces are unreadable since their creation.…”

Section: Guideline Adherencementioning

confidence: 90%

Single-state state machines in model-driven software engineering: an exploratory study

et al. 2021

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Context Models, as the main artifact in model-driven engineering, have been extensively used in the area of embedded systems for code generation and verification. One of the most popular behavioral modeling techniques is the state machine. Many state machine modeling guidelines recommend that a state machine should have more than one state in order to be meaningful. However, single-state state machines (SSSMs) violating this recommendation have been used in modeling cases reported in the literature. Objective We aim for understanding the phenomenon of using SSSMs in practice as understanding why developers violate the modeling guidelines is the first step towards improvement of modeling tools and practice. Method To study the phenomenon, we conducted an exploratory study which consists of two complementary studies. The first study investigated the prevalence and role of SSSMs in the domain of embedded systems, as well as the reasons why developers use them and their perceived advantages and disadvantages. We employed the sequential explanatory strategy, including repository mining and interview, to study 1500 state machines from 26 components at ASML, a leading company in manufacturing lithography machines from the semiconductor industry. In the second study, we investigated the evolutionary aspects of SSSMs, exploring when SSSMs are introduced to the systems and how developers modify them by mining the largest state-machine-based component from the company. Results We observe that 25 out of 26 components contain SSSMs. Our interviews suggest that SSSMs are used to interface with the existing code, to deal with tool limitations, to facilitate maintenance and to ease verification. Our study on the evolutionary aspects of SSSMs reveals that the need for SSSMs to deal with tool limitations grew continuously over the years. Moreover, only a minority of SSSMs have been changed between SSSM and multiple-state state machine (MSSM) during their evolution. The most frequent modifications developers made to SSSMs is inserting events with constraints on the execution of the events. Conclusions Based on our results, we provide implications for developers and tool builders. Furthermore, we formulate hypotheses about the effectiveness of SSSMs, the impacts of SSSMs on development, maintenance and verification as well as the evolution of SSSMs.

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Section: Guideline Adherencementioning

confidence: 90%

Single-state state machines in model-driven software engineering: an exploratory study

et al. 2021

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“…The readability of a program is closely related to its maintainability, making it a key factor in the overall quality of software [12]. Studies have shown that developers actively work to improve the readability of their code, demonstrating its importance in software development [13].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Code Readability through Automated Consistent Formatting

Kanoutas,

Karanikiotis,

Symeonidis

2024

Electronics

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Code readability is critical to software development and has a significant impact on maintenance and collaboration in evolving technology landscapes. With the increasing complexity of projects and the diversity of developers’ coding styles, the need for automated tools to improve code readability has become more apparent. This paper presents an innovative automated system designed to improve code readability by modeling and enforcing consistent formatting standards. The approach uses techniques such as Long Short-Term Memory (LSTM) networks and N-gram models, allowing the system to adapt to different coding styles and preferences. The system works autonomously by analyzing code styling within a project, identifying deviations from established standards and providing actionable recommendations for consistent styling. To validate our approach, several evaluations were performed on a large dataset of Java files. The results demonstrate the system’s effectiveness in detecting and correcting formatting errors, identifying a formatting error within the first five predictions more than 90% of the time, while providing the correct fix nearly 96% of the time, regardless of formatting convention or programming language. By offering a solution tailored to the specific needs of different teams, our system represents a significant advance in automated code formatting and readability improvement.

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“…However, do-while loops are easier to understand compared to while statements. Piantadosi et al [59] explored the effect of code evolution on code readability at the file level in 25 OSS. They concluded that only a very small number of commits impact the readability, and big commits that introduce new code can decrease readability.…”

Section: Related Workmentioning

confidence: 99%

Toward Understanding the Impact of Refactoring on Program Comprehension

Sellitto

Iannone

Codabux

et al. 2022

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

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Software refactoring is the activity associated with developers changing the internal structure of source code without modifying its external behavior. The literature argues that refactoring might have beneficial and harmful implications for software maintainability, primarily when performed without the support of automated tools. This paper continues the narrative on the effects of refactoring by exploring the dimension of program comprehension, namely the property that describes how easy it is for developers to understand source code. We start our investigation by assessing the basic unit of program comprehension, namely program readability. Next, we set up a large-scale empirical investigation -conducted on 156 opensource projects -to quantify the impact of refactoring on program readability. First, we mine refactoring data and, for each commit involving a refactoring, we compute (i) the amount and type(s) of refactoring actions performed and (ii) eight stateof-the-art program comprehension metrics. Afterwards, we build statistical models relating the various refactoring operations to each of the readability metrics considered to quantify the extent to which each refactoring impacts the metrics in either a positive or negative manner. The key results are that refactoring has a notable impact on most of the readability metrics considered.

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How does code readability change during software evolution?

Cited by 15 publications

References 51 publications

Single-state state machines in model-driven software engineering: an exploratory study

Single-state state machines in model-driven software engineering: an exploratory study

Enhancing Code Readability through Automated Consistent Formatting

Toward Understanding the Impact of Refactoring on Program Comprehension

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