• kalyani N. Suryawanshi
• Ashok N. Kamthane

Ultra-large scale mining has been shown to be useful for a number of Software Engineering tasks Such as Mining Specification,Defect Prediction. Extant techniques have dedicated on leveraging distributed computing to solve this problem, but with a concomitant increase in computational resource needs. This work proposes a technique that reduces the amount of calculation performed by the ultra-large-scale source code mining task, especially those that make use of control and data flow analyses. Our key idea is to analyze the mining task to identify and remove the irrelevant portions of the source code, prior to running the mining task. We show a recognition of our insight for mining and analyzing massive collections of control flow graphs of source codes. Our evaluation using 16 established control-/data-flow analyses that are typical components of mining tasks and 7 Million CFGs shows that our technique can achieve on average a 40% decline in the task calculation time. Our case studies demonstrates the applicability of our technique to massive scale source code mining tasks.

Source code analysis, Mining Software Repositories, Ultra-large-scale Mining, Data-driven Software Engineering,

"REVIEW ON ACCELERATING SOURCE CODE ANALYSIS AT MASSIVE SCALE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.94-99, June 2019, Available :http://www.jetir.org/papers/JETIR1906Z13.pdf

"REVIEW ON ACCELERATING SOURCE CODE ANALYSIS AT MASSIVE SCALE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp94-99, June 2019, Available at : http://www.jetir.org/papers/JETIR1906Z13.pdf