چکیده مقاله
One of the most important goals of fault prediction is to detect fault prone modules as early as possible in the software development life cycle Early detection of software faults could lead to reduced development costs and rework effort and more reliable software So, the study of the fault prediction is important to achieve software quality Different data mining algorithms are used to extract fault prone modules In this survey we will discuss data mining techniques that are association mining, classification and clustering for software fault prediction This helps the developers to detect software faults and correct them
کلیدواژهها
نویسندگان
Zahra Rahmani Ghobadi
Department of Computer Qazvin Branch, Islamic Azad University
Hasan Rashidi Heramabadi
Department of Mathematics and Computer Science Allameh Tabataba'i University Tehran, Iran
شیوه ارجاع
Rahmani Ghobadi, Zahra and Rashidi Heramabadi, Hasan,1393,A Survey of Data Mining Techniques for SoftwareFault Prediction,8th International Conference on e-Commerce with focus on E-Trust,Mashhad
ارائهشده در
مجموعه مقالات نهمین کنفرانس بین المللی تجارت الکترونیک با رویکرد بر کسب و کار الکترونیکی26 فروردین 1394 · مشهد