Tool assisted identifier naming for improved software readability: an empirical study

Relf, Phillip

doi:10.1109/isese.2005.1541814

Cited by 18 publications

(15 citation statements)

References 24 publications

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“…A consensus exists that readability is an essential determining characteristic of code quality [1,5,10,11,19,29,34,35,36,37,38,44], but not about which factors most contribute to human notions of software readability. A previous study by Tenny looked at readability by testing comprehension of several versions of a program [42].…”

Section: Human Readability Annotationmentioning

confidence: 99%

“…We hypothesize that everyone who has written code has some intuitive notion of this concept, and that program features such indentation (e.g., as in Python [43]), choice of identifier names [37], and comments are likely to play a part. Dijkstra, for example, claimed that the readability of a program depends largely upon the simplicity of its sequencing control, and employed that notion to help motivate his topdown approach to system design [10].…”

Section: Introductionmentioning

confidence: 99%

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A metric for software readability

Buse

Weimer

2008

Proceedings of the 2008 International Symposium on Software Testing and Analysis

120

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In this paper, we explore the concept of code readability and investigate its relation to software quality. With data collected from human annotators, we derive associations between a simple set of local code features and human notions of readability. Using those features, we construct an automated readability measure and show that it can be 80% effective, and better than a human on average, at predicting readability judgments. Furthermore, we show that this metric correlates strongly with two traditional measures of software quality, code changes and defect reports. Finally, we discuss the implications of this study on programming language design and engineering practice. For example, our data suggests that comments, in of themselves, are less important than simple blank lines to local judgments of readability.

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Section: Human Readability Annotationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A metric for software readability

Buse

Weimer

2008

Proceedings of the 2008 International Symposium on Software Testing and Analysis

120

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“…Users preferred "logical variable names", "declaring variable names to represent what they do/are", "clear naming of variables", and variables demonstrating "standard naming". The importance of identifier names has been previously studied (e.g., [30]). Our algorithm tracks common variable naming information from concrete source code to produce understandable variable names.…”

Section: Survey Resultsmentioning

confidence: 99%

“…A consensus exists that readability is an essential determining characteristic of code quality [28,77,5,31,61,64,73,78,7,30,8,72], but not about which factors most contribute to human notions of software readability. A previous study by Tenny looked at readability by testing comprehension of several versions of a program [79].…”

Section: Study Methodologymentioning

confidence: 99%

“…In the software domain, this is a critical determining factor of quality [28]. Perhaps everyone who has written code has an intuitive notion of the concept of software readability, and that program features such as indentation (e.g., as in Python [29]), choice of identifier names [30], and comments play a significant part. Dijkstra, for example, claimed that the readability of a program depends largely upon the simplicity of its sequencing control, and employed that notion to help motivate his top-down approach to system design [31].…”

Section: Modeling Code Readabilitymentioning

confidence: 99%

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Automatically Describing Program Structure and Behavior

Buse

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Powerful development environments, data-rich project management systems, and ubiquitous software frameworks have fundamentally altered the way software is constructed and maintained. Today, professional developers spend less than 10% of their time actually writing new code and instead primarily try to understand existing software [1,2]. Increasingly, programmers work by searching for examples or other documentation and then assembling pre-constructed components. Yet, code comprehension is poorly understood and documentation is often incomplete, incorrect, or unavailable [3,4]. Moreover, few tools exist to support structured code search making the process of finding useful examples ad hoc, slow and error prone.The overarching goal of this research is to help humans better understand software at many levels and in doing so improve development productivity and software quality. The contributions of this work are in three areas: readability, runtime behavior, and documentation.We introduce the first readability metric for program source code. Our model for readability, which is internally based on logistic regression, was learned from data gathered from 120 study participants. Our model agrees with human annotators as much as they agree with each other. The importance of measuring code readability is highlighted by the observation that reading code is now the most time consuming part [5,6,7,8] of the most expensive activity [9,10] in the software development process.Beyond surface-level readability, understanding software demands understanding the behavior of the running program. To that end, we contribute a model describing runtime behavior in terms of program paths. Our approach is based on two key insights. First, we observe a relationship between the relative frequency of a path and its effect on program state. Second, we carefully choose the right level of abstraction for considering program paths: interprocedural for precision, but limited to one class for scalability. Over several benchmarks, the top 5% of paths as ranked by our algorithm account for over half of program runtime.Finally, we describe automated approaches for improving the understandability of software through documentation synthesis. A 2005 NASA survey found that the most significant barrier to code reuse is that software is too difficult to understand or is poorly documented [11]. As software systems grow ever larger and more complex, we believe that automated documentation tools will become indispensable. The key i Chapter 0Abstract ii to our approach is adapting programming language techniques (e.g., symbolic execution) to create output that is directly comparable to existing human-written artifacts. This has two key advantages: (1) it simplifies evaluation by permitting objective comparisons to existing documentation, and (2) it enables tools to be used immediately -no significant change to the development process is required. We describe and evaluate documentation synthesis algorithms for exceptions, code changes, and APIs. In eac...

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Automatic Segmentation of Method Code into Meaningful Blocks: Design and Evaluation

Wang

Pollock

Vijay‐Shanker

2013

J Software Evolu Process

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SUMMARYGood programming practice and guidelines suggest that programmers use both vertical and horizontal spacing to visibly delineate between code segments that represent different algorithmic steps or high-level actions. Unfortunately, programmers do not always follow these guidelines. Editors and integrated development environments (IDEs) can easily indent codes based on syntax, but they do not currently support automatic blank line insertion, which presents more significant challenges involving the semantics.This paper presents and evaluates a heuristic solution to the automatic blank line insertion problem by leveraging both program structure and naming information to identify 'meaningful blocks', consecutive statements that logically implement a high-level action. Our tool, SEGMENT, takes as input a Java method and outputs a segmented version that separates meaningful blocks by vertical spacing. We report on several studies involving human judgments to evaluate the effectiveness of the automatic blank line insertion algorithm, for different size methods and for different levels of programmer expertise. The results indicate strong positive overall opinion of SEGMENT's effectiveness in comparison with both developer-written blank lines and blank lines inserted by newcomers to the code. The results vary only slightly among short and long methods, and among novice and advanced programmers. SEGMENT assists in making users obtain an overall picture of a method's actions and comprehend it quicker as well as provides hints for internal documentation placement.

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Tool assisted identifier naming for improved software readability: an empirical study

Cited by 18 publications

References 24 publications

A metric for software readability

A metric for software readability

Automatically Describing Program Structure and Behavior

Automatic Segmentation of Method Code into Meaningful Blocks: Design and Evaluation

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