Does the Role Matter? An Investigation of the Code Quality of Casual Contributors in GitHub

2016 ◽  
Author(s):  
Yao Lu ◽  
Xinjun Mao ◽  
Zude Li ◽  
Yang Zhang ◽  
Tao Wang ◽  
...  
Keyword(s):  
Code Quality

scholarly journals Judging a commit by its cover; or can a commit message predict build failure?

2016 ◽  
Author(s):  
Eddie A Santos ◽  
Abram Hindle
Keyword(s):  
Open Source ◽  
Language Models ◽  
Cross Entropy ◽  
N Gram ◽  
Code Quality

Developers summarize their changes to code in commit messages. When a message seems “unusual,” however, this puts doubt into the quality of the code contained in the commit. We trained \(n\)-gram language models and used cross-entropy as an indicator of commit message “unusualness” of over 120 000 commits from open source projects. Build statuses collected from Travis-CI were used as a proxy for code quality. We then compared the distributions of failed and successful commits with regards to the “unusualness” of their commit message. Our analysis yielded significant results when correlating cross-entropy with build status.

scholarly journals Effects of Training on Quality of Clinical Coding at Mbagathi County Referral Hospital, Nairobi City County, Kenya

2018 ◽  
Vol 6 (1) ◽  
pp. 231-237
Author(s):  
Josephat Gachoka Kiongo ◽  
Otieno G. O. ◽  
Yitambe A. O
Keyword(s):  
Health Sector ◽  
Poor Quality ◽  
Referral Hospital ◽  
Clinical Coding ◽  
Code Assignment ◽  
Coding Quality ◽  
Process And Outcome ◽  
Code Completion ◽  
Code Quality

Introduction: Professionals from various cadres in the health sector raise concerns regarding the poor quality of clinical coding leading to lack of evidence-based practice. Assessing the quality of the clinical coding in one of Nairobi City County’s major hospital would be a step towards establishing the exact gaps in quality of the coding process and outcome. Training the professionals would also foster better clinical coding practice in one of the major facilities nationally. Method: The study aimed at establishing the quality of clinical coding within Mbagathi County Referral Hospital, and thereafter determined the effect of training on the established clinical coding quality. An interventional trial study design was used, with a quality of clinical coding checklist used classify codes assignment or lack of which. The sample included 320 patient files selected randomly from a month-long list of patients. Results: The study found out that the overall baseline code quality was slightly above average given that majority (55%) of the code assignment were good as established by a composite score of the various coding quality attributes assessed. Given the need for training based on the low quality, a training intervention was then conducted based on the needs identified. An indexing database was also installed for the coders to use in encoding the codes assigned. Code quality improved to 77% after the training. Code completion was excellent at the facility, as established from the 97% of the files that were completely coded at baseline and later improved to 99%. Notably, also, is that the hospital improved its coding of procedures and death certification by 32 and 53% respectively. The hospital also started using the indexing tool that was introduced as an intervention. Conclusions: The health facility could act as a good benchmark for code completion. However, code completion without accuracy in the code assignment invalidates the overall quality of coding. Code accuracy improved with the training almost immediately after the interventions. More practice would for sure lead to better clinical coding accuracy.  

scholarly journals The State of Software in Evolutionary Biology

2015 ◽  
Author(s):  
Diego Darriba ◽  
Tomas Flouri ◽  
Alexandros Stamatakis
Keyword(s):  
Software Quality ◽  
Evolutionary Biology ◽  
Current Data ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Scientific Software ◽  
Code Quality ◽  
Generation Sequencing ◽  
Highly Cited

With Next Generation Sequencing Data (NGS) coming off age and being routinely used, evolutionary biology is transforming into a data-driven science. As a consequence, researchers have to rely on a growing number of increasingly complex software. All widely used tools in our field have grown considerably, in terms of the number of features as well as lines of code. In addition, analysis pipelines now include substantially more components than 5-10 years ago. A topic that has received little attention in this context is the code quality of widely used codes. Unfortunately, the majority of users tend to blindly trust software and the results it produces. To this end, we assessed the code quality of 15 highly cited tools (e.g., MrBayes, MAFFT, SweepFinder etc.) from the broader area of evolutionary biology that are used in current data analysis pipelines. We also discuss widely unknown problems associated with floating point arithmetics for representing real numbers on computer systems. Since, the software quality of the tools we analyzed is rather mediocre, we provide a list of best practices for improving the quality of existing tools, but also list techniques that can be deployed for developing reliable, high quality scientific software from scratch. Finally, we also discuss journal and science policy as well as funding issues that need to be addressed for improving software quality as well as ensuring support for developing new and maintaining existing software. Our intention is to raise the awareness of the community regarding software quality issues and to emphasize the substantial lack of funding for scientific software development.

scholarly journals Developing A Code Readability Model To Improve Software Quality

2012 ◽  
pp. 206-210
Author(s):  
Venkatesh Podugu
Keyword(s):  
Graphical User Interface ◽  
Software Quality ◽  
Software Maintenance ◽  
Software Metrics ◽  
Software Defect ◽  
Code Changes ◽  
Software Code ◽  
The Many ◽  
Code Quality

Software maintenance is one of the main phase in software evaluation. This paper presents the relation between software metrics and maintainability. This paper explains about the concept of Software code readability and its relation to software quality. The quality of code is very essential for the future and for the reuse purpose. Here generated a code readability model to calculate the readability of the code by selecting the snippets and these snippets are to be given to the expert to rate them. Collecting the features of code and combing the judgments generated the readability model. This paper focus on providing the graphical user interface (GUI),to the code readability model to improve the understanding of software code readability. By providing the readability of code to the many open source projects, automatically informing the existed code quality to improve the quality of code. It show that this readability model developed is correlates strongly with three measures of software quality: code changes in software, defect log messages and automated defect reports. It measures correlations over many releases of selected projects.

scholarly journals Quality of medical information determine the quality of diagnosis code

2019 ◽  
Vol 8 (3) ◽  
pp. 326
Author(s):  
Warsi Maryati ◽  
Indriyati Oktaviano Rahayuningrum ◽  
Ani Ismayani
Keyword(s):  
Patient Care ◽  
Medical Information ◽  
Diagnosis Code ◽  
High Quality ◽  
Chi Square ◽  
Chi Square Test ◽  
Log Odds ◽  
Code Quality ◽  
Care Costs

<span>The accuracy of the diagnosis code has implications for future patient care planning, provision of health services and patient care costs. Therefore, <br /> this study has analyzed the influence of the quality of medical information on the quality of the diagnosis code which includes the accuracy, consistency, completeness and timeliness in coding the diagnosis of inpatients at <br /> Dr. Moewardi hospital.</span><span>This was an observational analytic study with <br /> a sample of 250 medical records taken using stratified random sampling. Data were analyzed by chi square test</span><span lang="IN">. </span><span>High quality of medical information has a better diagnosis code quality (73.80%) compared to poorly quality of medical information (36.00%). High quality of medical information has a log odds of 1.54 better in the quality of diagnosis code than poorly quality of medical information (b=1.54; 95% CI=0.81-2.27, p&lt;0.001).</span>

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