Quality of Medical Information Determine the Quality of Diagnosis Code

The accuracy of the diagnosis code has implications for future patient care planning, provision of health services and patient care costs. Therefore, 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 Dr. Moewardi hospital.This was an observational analytic study with a sample of 250 medical records taken using stratified random sampling. Data were analyzed by chi square test. 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<0.001)

Is the Quality of Numerical Subroutine Code Improving?

We begin by using a software metric tool to generate a number of software complexity measures and we investigate how these values may be used to determine subroutines which are likely to be of substandard quality. Following this we look at how these metric values have changed over the years. First we consider a number of freely available Fortran libraries (Eispack, Linpack and Lapack) which have been constructed by teams. In order to ensure a fair comparison we use a restructuring tool to transform original Fortran 66 code into Fortran 77. We then consider the Fortran codes from the Collected Algorithms from the ACM (CALGO) to see whether we can detect the same trends in software written by the general numerical community. Our measurements show that although the standard of code in the freely available libraries does appear to have improved over time these libraries still contain routines which are effectively unmaintainable and untestable. Applied to the CALGO codes the metrics indicate a very conservative approach to software engineering and there is no evidence of improvement, during the last twenty years, in the qualities under discussion

Exact Gap Computation for Code Coverage Metrics in ISO-C

Test generation and test data selection are difficult tasks for model based testing. Tests for a program can be meld to a test suite. A lot of research is done to quantify the quality and improve a test suite. Code coverage metrics estimate the quality of a test suite. This quality is fine, if the code coverage value is high or 100%. Unfortunately it might be impossible to achieve 100% code coverage because of dead code for example. There is a gap between the feasible and theoretical maximal possible code coverage value. Our review of the research indicates, none of current research is concerned with exact gap computation. This paper presents a framework to compute such gaps exactly in an ISO-C compatible semantic and similar languages. We describe an efficient approximation of the gap in all the other cases. Thus, a tester can decide if more tests might be able or necessary to achieve better coverage.Comment: In Proceedings MBT 2012, arXiv:1202.582

Influence of developer factors on code quality: a data study

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Automatic source-code inspection tools help to assess, monitor and improve code quality. Since these tools only examine the software project’s codebase, they overlook other possible factors that may impact code quality and the assessment of the technical debt (TD). Our initial hypothesis is that human factors associated with the software developers, like coding expertise, communication skills, and experience in the project have some measurable impact on the code quality. In this exploratory study, we test this hypothesis on two large open source repositories, using TD as a code quality metric and the data that may be inferred from the version control systems. The preliminary results of our statistical analysis suggest that the level of participation of the developers and their experience in the project have a positive correlation with the amount of TD that they introduce. On the contrary, communication skills have barely any impact on TD.Peer ReviewedPostprint (author's final draft

Synthesizing Certified Code

Code certification is a lightweight approach for formally demonstrating software quality. Its basic idea is to require code producers to provide formal proofs that their code satisfies certain quality properties. These proofs serve as certificates that can be checked independently. Since code certification uses the same underlying technology as program verification, it requires detailed annotations (e.g., loop invariants) to make the proofs possible. However, manually adding annotations to the code is time-consuming and error-prone. We address this problem by combining code certification with automatic program synthesis. Given a high-level specification, our approach simultaneously generates code and all annotations required to certify the generated code. We describe a certification extension of AutoBayes, a synthesis tool for automatically generating data analysis programs. Based on built-in domain knowledge, proof annotations are added and used to generate proof obligations that are discharged by the automated theorem prover E-SETHEO. We demonstrate our approach by certifying operator- and memory-safety on a data-classification program. For this program, our approach was faster and more precise than PolySpace, a commercial static analysis tool