scholarly journals Defect Forecasting by Specification Mining Using Code Quality Metrics

Author(s):  
K. HEMANTH KUMAR ◽  
D. MURAHARI REDDY
Stroke ◽  
2019 ◽  
Vol 50 (Suppl_1) ◽  
Author(s):  
Laurel Packard ◽  
Hattie LaCroix ◽  
Tricia Tubergen ◽  
Cuyler Huffman ◽  
Bassel Raad ◽  
...  

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Heather Martin ◽  
Laurel Packard ◽  
Danielle Gritters ◽  
Hattie LaCroix ◽  
Tricia Tubergen ◽  
...  

Background: Advanced Practice Providers (APPs) are important members of stroke code teams. However, the impact of APP involvement on quality metrics and functional outcomes is unclear. We sought to evaluate if APPs perform similarly to neurology residents for stroke code quality metrics and functional outcome at 90 days. Methods: We retrospectively analyzed data of consecutive patients who underwent thrombectomy in a single center cohort. Demographics, National Institute of Health Stroke Scale (NIHSS), last known normal (LKN) to emergency department (ED) presentation time, ED door to skin puncture time, recanalization (mTICI IIb/III) rates, and modified rankin scale (mRS) at 90 days were compared between neurology residents and APPs. A multiple logistic regression was used to determine factors independently associated with a favorable mRS at 90 days. Results: A total of 172 patients were included in the study of which 80 (47%) were managed by neurology residents. Both groups (residents vs. APPs) were balanced for age ( p =0.87), NIHSS ( p =0.18), LKN to ED Door time ( p =0.19), ED door to skin puncture time ( p =0.08), recanalization rate ( p =0.28), and favorable outcome (mRS 0-2) ( p =0.27). The multiple logistic regression model found patients with recanalization were 8.9 times more likely to have a favorable outcome. Age and initial NIHSS were found to be negative predictors of mRS (Table 1). Resident or APP involvement in the stroke code process did not impact outcome ( p =0.08). Conclusion: APPs achieve similar acute stroke code metrics and functional outcomes when compared to neurology residents. Further studies are needed to confirm our findings.


Author(s):  
Gerardo Canfora ◽  
Andrea Di Sorbo ◽  
Francesco Mercaldo ◽  
Corrado Aaron Visaggio

Author(s):  
Vladik Kreinovich ◽  
Omar A. Masmali ◽  
Hoang Phuong Nguyen ◽  
Omar Badreddin ◽  
◽  
...  

Millions of lines of code are written every day, and it is not practically possible to perfectly thoroughly test all this code on all possible situations. In practice, we need to be able to separate codes which are more probable to contain bugs – and which thus need to be tested more thoroughly – from codes which are less probable to contain flaws. Several numerical characteristics – known as code quality metrics – have been proposed for this separation. Recently, a new efficient class of code quality metrics have been proposed, based on the idea to assign consequent integers to different levels of complexity and vulnerability: we assign 1 to the simplest level, 2 to the next simplest level, etc. The resulting numbers are then combined – if needed, with appropriate weights. In this paper, we provide a theoretical explanation for the above idea.


Author(s):  
A.K. IAVARASI ◽  
S. AARTHY

classification is the problem of identifying a set of categories to a new comments.To improve the efficiency of the code,quality metrics are applied for evaluation.The binary classifier,predicts the false positive rates with lesser accuracy,and limited number of classes only to predict the accuracy for classifier.To address this problem,support vector machine classifier is used,which helps in detecting the false positive rates,improving code quality and the accuracy will also increased.


2017 ◽  
Vol 5 ◽  
pp. 200-203
Author(s):  
Mariusz Łukasik ◽  
Marek Miłosz

In agile methods, one of the techniques for improving code quality is refactoring. This is a process that employs a number of techniques, modifying the code without changing its functionality, aiming to improve its transparency and reduce vulnerability. You can measure the improvement of the code using different code quality metrics. The paper presents an analysis of the effect of refactoring on static code quality on the example of the open-source project Scuba. The quality of the code was measured at two different points of software development - right before and after refactoring the code. The three most popular sets of object code quality metrics and the Sonarqube tool were used for the measurement. The research clearly demonstrates the significant improvement of static code quality as a result of refactoring.


2012 ◽  
Vol 38 (1) ◽  
pp. 175-190 ◽  
Author(s):  
Claire Le Goues ◽  
Westley Weimer

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