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13 results for “Code Readability”
On the Use of Artificially Degraded Manuscripts for Quality Assessment of Readability Enhancement Methods - Dataset & Code
<p>This object contains the dataset and python code used for the paper:</p> <p>S. Brenner and R. Sablatnig. On the Use of Artificially Degraded Manuscripts for Quality Assessment of Readability Enhancement Methods. Accepted for OAGM Workshop 2019<strong>, </strong>Steyr, Austria.</p> <p>The dataset is a modified subset of the UCL Multispectral Processed Images of Parchment Damage Dataset (<a href="http://dx.doi.org/10.14324/000.ds.1469099">10.14324/000.ds.1469099</a>). The accompanying code documents how the modified version was created and how the evaluations described in the paper were performed.</p>
To What Extent Cognitive-Driven Development Improves Code Readability?
<p>Cognitive-Driven Development (CDD) is a coding design technique<br> that aims to reduce the cognitive effort that developers place in<br> understanding a given code unit (e.g., a class). By following CDD de-<br> sign practices, it is expected that the coding units to be smaller, and,<br> thus, easier to maintain and evolve. However, it is so far unknown<br> whether these smaller code units coded using CDD standards are,<br> indeed, easier to understand. In this work we aim to assess to what<br> extent CDD improves code readability. To achieve this goal, we<br> conducted a two-phase study. We start by inviting professional<br> software developers to vote (and justify their rationale) on the most<br> readable pair of code snippets (from a set of 10 pairs); one of the<br> pairs was coded using CDD practices. We received 133 answers.<br> In the second phase, we applied the state-of-the art readability<br> model on the 10-pairs of CDD-driven refactorings. We observed<br> some conflicting results. On the one hand, developers perceived<br> that seven (out of 10) CDD-driven refactorings were more readable<br> than their counterparts; for two other CDD-driven refactorings,<br> developers were undecided, while only in one of the CDD-driven<br> refactorings, developers preferred the original code snippet. On<br> the other hand, we noticed that only one CDD-driven refactorings<br> have better performance readability, assessed by state-of-the-art<br> readability models. Our results provide initial evidence that CDD<br> could be an interesting approach for software design</p>
Replication Package for "Improving the Readability of Generated Tests Using GPT-4 and ChatGPT Code Interpreter"
<p>While automated test generation can decrease the human burden associated with testing, it does not eliminate this burden. Humans must still work with generated test cases to interpret testing results, debug the code, build and maintain a comprehensive test suite, and many other tasks. Therefore, a major challenge with automated test generation is understandability of generated test test cases. </p> <p>Large language models (LLMs), machine learning models trained on massive corpora of textual data - including both natural language and programming languages - are an emerging technology with great potential for performing language-related predictive tasks such as translation, summarization, and decision support. </p> <p>In this study, we are exploring the capabilities of LLMs with regard to improving test case understandability.</p> <p>This package contains the data produced during this exploration:</p> <ul> <li>The examples directory contains the three case studies we tested our transformation process on: <ul> <li>queue_example: Tests of a basic queue data structure</li> <li>httpie_sessions: Tests of the sessions module from the httpie project. </li> <li>string_utils_validation: Tests of the validation module from the python-string-utils project.</li> <li>Each directory contains the modules-under-test, the original test cases generated by Pynguin, and the transformed test cases. </li> <li>Two trials were performed per case example of the transformation technique to assess the impact of different results from the LLM.</li> </ul> </li> <li>The survey directory contains the survey that was sent to assess the impact of the transformation on test readability. <ul> <li>survey.pdf contains the survey questions.</li> <li>responses.xlsx contains the survey results.</li> </ul> </li> </ul>
Machine readable code lists for an algorithm to identify incident non-small cell lung cancer (NSCLC) in United States healthcare claims data
<p>Machine readable code lists for an algorithm to identify incident non-small cell lung cancer (NSCLC) in United States healthcare claims data</p>
DATASET - On the Investigation of Empirical Contradictions - Aggregated Results of Local Studies on Readability and Comprehensibility of Source Code
<p>Study package containing raw and analyzed data from the work entitled "On the Investigation of Empirical Contradictions - Aggregated Results of Local Studies on Readability and Comprehensibility of Source Code".</p> <p>The package comprises: 1) a summary of the information extracted from all papers mentioned in the Background; 2) the source code snippets used in the three studies; 3) the consent and characterization forms distributed to the participants; 4) the raw data, the aggregated data and other material generated with the collected data.</p>
Enhancing Code Readability through Automated Consistent Formatting
<p>The provided dataset contains the data used by "Enhancing Code Readability through Automated Consistent Formatting", in order to train a personalized code formatter based on the styling used by a team of developers in a set of code files.</p>
Replication Package of "Developer-Centric Code Readability Assessment: Are We There Yet?"
<p>This repository contains the datasets and the scripts to replicate our work "Developer-Centric Code Readability Assessment: Are We There Yet?".</p>
Impacts of Coding Practices on Readability - Dataset
<p>Data gathered and analyzed as described on the paper "Impacts of Coding Practices on Readability", presented at the ICPC 2018</p>
Assessing Code Readability in Python Programming Courses Using Eye-Tracking - Python Code Snippets
<p>Python code snippets for assessing code readability in Python programming courses using eye-tracking.</p>
The code and data for paper "Reassessing Java Code Readability Models with a Human-Centered Approach"
<p>The code and data for paper "Reassessing Java Code Readability Models with a Human-Centered Approach".<br> Please refer to README.md for detailed instructions.</p>
On the Importance and Shortcomings of Code Readability Metrics: A Case Study on Reactive Programming - replication package
<p>This is the replication package for the conference paper submission "On the Importance and Shortcomings of Code Readability Metrics: A Case Study on Reactive Programming"</p> <p><strong>Contents:</strong></p> <ul> <li>measurements.zip <ul> <li>DATASET_ORIGINAL.csv</li> <li>DATASET_REACTIVE.csv</li> </ul> </li> <li>source_code.zip <ul> <li> source_code_orig <ul> <li>Client.java</li> <li>Connection.java</li> <li>Server.java</li> <li>TcpConnection.java</li> <li>UdpConnection.java</li> </ul> </li> <li> source_code_rx <ul> <li>Client.java</li> <li>Connection.java</li> <li>Server.java</li> <li>TcpConnection.java</li> <li>UdpConnection.java</li> </ul> </li> </ul> </li> </ul> <p> </p>
The code and data for paper "Reassessing Java Code Readability Models with a Human-Centered Approach"
<p>The code and data for paper "Reassessing Java Code Readability Models with a Human-Centered Approach".<br> Please refer to README.md for detailed instructions.</p>
Machine readable code lists for an algorithm to identify incident lung cancer in United States healthcare claims data
<p>Machine readable code lists for an algorithm to identify incident lung cancer in United States healthcare claims data</p>
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