By Jeffrey J P Tsai, Du Zhang
Computing device studying bargains with the difficulty of ways to construct computing device courses that enhance their functionality at a few projects via adventure. laptop studying algorithms have confirmed to be of serious useful worth in quite a few software domain names. no longer unusually, the sphere of software program engineering seems to be a fertile flooring the place many software program improvement and upkeep projects may be formulated as studying difficulties and approached when it comes to studying algorithms. This booklet offers with the topic of desktop studying purposes in software program engineering. It offers an outline of desktop studying, summarizes the state-of-the-practice during this area of interest zone, provides a class of the prevailing paintings, and provides a few program instructions. additionally integrated within the publication is a suite of formerly released papers during this examine region.
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Additional info for Machine Learning Applications In Software Engineering (Series on Software Engineering and Knowledge Engineering)
Note (see Fig. 1 on the following page) that with slightly more than 50 of the 83 datapoints for RUSE being set at Nominal and with no observations at XH, the data for RUSE does not exhibit enough dispersion along the entire range of possible values for RUSE. While this is the familiar errors-in-variables problem, our data doesn't allow us to resolve this difficulty. Thus, the authors were forced to assume that the random variation in the responses for RUSE is small compared to the range of RUSE.
Chapter 8 is concerned with how ML methods are used to capture and manage software development or process knowledge. The one paper in the chapter discusses a CBR based method for collecting and managing software development knowledge as it evolves in an organizational context. Finally, Chapter 9 offers some guidelines on how to select ML methods for SE tasks, how to formulate an SE task into a learning problem, and concludes the book with remarks on where future effort will be needed in this niche area.
Category Application Prediction Quality Size Development effort Maintenance effort Reliability Release time Testability Discovery Identifying objects Process models Transformation Modularity Acquisition Specification refinement 30 Table 13. DT applications. Category Application Prediction Quality Development cost Development effort Maintenance effort Resource analysis Correction cost Reusability Generation Project management rules Reuse Cost of rework Acquisition Specification refinement Table 14.