Identifying the Software Failure Mechanisms Using Data Mining Techniques
Nadhem Sultan Ali Ebrahim1, V.P Pawar2
1Nadhem Sultan Ali Ebrahim, SRTM University Department of Computational Science Nanded Mahrashtra, India.
2Dr.V.P Pawar, SRTM University Department of Computational Science Nanded Mahrashtra, India.
Manuscript received on August 04, 2013. | Revised Manuscript received on August 27, 2013. | Manuscript published on September 05, 2013. | PP: 30-32 | Volume-3, Issue-4, September 2013. | Retrieval Number: C1733073313/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Software is ubiquitous in our daily life. It brings us great convenience and a big headache about software reliability as well: Software is never bug-free, and software bugs keep incurring monetary loss or even catastrophes. In the pursuit of better reliability, software engineering researchers found that huge amount of data in various forms can be collected from software systems, and these data, when properly analyzed, can help improve software reliability. Unfortunately, the huge volume of complex data renders simple analysis techniques incompetent; consequently, Studies have been resorting to data mining for more effective analysis. In the past few years, we have witnessed many studies on mining for software reliability reported in data mining as well as software engineering forums. These studies either develop new or apply existing data mining techniques to tackle reliability problems from different angles. In order to keep data mining researchers abreast of the latest development in this growing research area, we propose this Paper on mining for software reliability.
Keywords: Reliability, Techniques.