NDLI: The potential benefit of relevance vector machine to software effort estimation

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The potential benefit of relevance vector machine to software effort estimation

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The potential benefit of relevance vector machine to software effort estimation

Content Provider	ACM Digital Library
Author	Song, Liyan Minku, Leandro L. Yao, Xin
Abstract	Three key challenges faced by the task of software effort estimation (SEE) when using predictive models are: (1) in order to support decision-making, software managers should have access not only to the effort estimation given by the predictive model, but also how confident this model is in estimating a given project and how likely other effort values could be the real efforts required to develop this project, (2) SEE data is likely to contain noise, due to the participation of humans in the data collection, and this noise can hinder predictions if not catered, and (3) data collection is an expensive task, and guidelines on when new data need to be collected would be helpful for reducing the cost associated with data collection. However, even though SEE has been studied for decades and many predictors have been proposed, few methods focus on these issues. In this work, we show that relevance vector machine (RVM) is a promising predictive method for addressing these three challenges. More specifically, it explicitly handles noise, it provides probabilistic predictions of effort, and can be used to identify when the required efforts of new projects should be collected for using them as training examples. With that in mind, this work provides the first step in exploiting RVM's potential for SEE by validating both its point prediction and prediction intervals. It then explains in detail future directions in terms of how RVMs can be further exploited for addressing the above mentioned challenges. Our systematic experiments show that RVM is very competitive compared with state-of-the-art SEE approaches, being usually ranked the first or second in 7 across 11 data sets in terms of mean absolute error. We also demonstrate how RVM can be used to judge the amount of noise present in the data. In summary, we show that RVM is a very promising predictor for SEE and should be further exploited.
Starting Page	52
Ending Page	61
Page Count	10
File Format	PDF
ISBN	9781450328982
DOI	10.1145/2639490.2639510
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-09-17
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Effort noise Machine learning Relevance vector machine Software effort estimation Data collection guidance Prediction interval
Content Type	Text
Resource Type	Article

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