Asset Details
Do you wish to reserve the book?
A decision tree logic based recommendation system to select software fault prediction techniques
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
A decision tree logic based recommendation system to select software fault prediction techniques
Do you wish to request the book?
A decision tree logic based recommendation system to select software fault prediction techniques
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
A decision tree logic based recommendation system to select software fault prediction techniques
2017
Overview
Identifying a reliable fault prediction technique is the key requirement for building effective fault prediction model. It has been found that the performance of fault prediction techniques is highly dependent on the characteristics of the fault dataset. To mitigate this issue, researchers have evaluated and compared a plethora of fault prediction techniques by varying the context in terms of domain information, characteristics of input data, complexity, etc. However, the lack of an accepted benchmark makes it difficult to select fault prediction technique for a particular context of prediction. In this paper, we present a recommendation system that facilitates the selection of appropriate technique(s) to build fault prediction model. First, we have reviewed the literature to elicit the various characteristics of the fault dataset and the appropriateness of the machine learning and statistical techniques for the identified characteristics. Subsequently, we have formalized our findings and built a recommendation system that helps in the selection of fault prediction techniques. We performed an initial appraisal of our presented system and found that proposed recommendation system provides useful hints in the selection of the fault prediction techniques.
Publisher
Springer Vienna,Springer Nature B.V
