scholarly journals Detecting and Ranking API Usage Pattern in Large Source Code Repository: A LFM Based Approach

Author(s):  
Jitong Zhao ◽  
Yan Liu
Author(s):  
Evan Moritz ◽  
Mario Linares-Vasquez ◽  
Denys Poshyvanyk ◽  
Mark Grechanik ◽  
Collin McMillan ◽  
...  
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Author(s):  
Sara McCaslin ◽  
Kent Lawrence

Closed-form solutions, as opposed to numerically integrated solutions, can now be obtained for many problems in engineering. In the area of finite element analysis, researchers have been able to demonstrate the efficiency of closed-form solutions when compared to numerical integration for elements such as straight-sided triangular [1] and tetrahedral elements [2, 3]. With higher order elements, however, the length of the resulting expressions is excessive. When these expressions are to be implemented in finite element applications as source code files, large source code files can be generated, resulting in line length/ line continuation limit issues with the compiler. This paper discusses a simple algorithm for the reduction of large source code files in which duplicate terms are replaced through the use of an adaptive dictionary. The importance of this algorithm lies in its ability to produce manageable source code files that can be used to improve efficiency in the element generation step of higher order finite element analysis. The algorithm is applied to Fortran files developed for the implementation of closed-form element stiffness and error estimator expressions for straight-sided tetrahedral finite elements through the fourth order. Reductions in individual source code file size by as much as 83% are demonstrated.


2015 ◽  
Vol 3 (2) ◽  
pp. 13-23
Author(s):  
Yuki Ito ◽  
Atsuo Hazeyama ◽  
Yasuhiko Morimoto ◽  
Hiroaki Kaminaga ◽  
Shoichi Nakamura ◽  
...  

In order to extend and maintenance software systems, it is necessary to remove factors behind bad smells from source code through refactoring. However, it is time-consuming process to detect and remove factors behind bad smells manually from large source code. And, learning how to refactor bad smells can be difficult for students because they are not yet software development experts. Therefore, the authors propose a method for detecting bad smells using declarative meta programming that can be applied to software development training. In this manner, software development training is facilitated.


2021 ◽  
pp. 299-319
Author(s):  
Xue-er Ding ◽  
Jun Niu ◽  
Jia Wang

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