By Jeffrey J P Tsai, Du Zhang
Computing device studying offers with the problem of ways to construct machine courses that increase their functionality at a few projects via event. computer studying algorithms have confirmed to be of significant functional price in a number of program domain names. now not unusually, the sector of software program engineering seems to be a fertile flooring the place many software program improvement and upkeep initiatives can be formulated as studying difficulties and approached by way of studying algorithms. This booklet offers with the topic of laptop studying functions in software program engineering. It presents an outline of computer studying, summarizes the state-of-the-practice during this area of interest quarter, provides a type of the present paintings, and provides a few program directions. additionally integrated within the publication is a suite of formerly released papers during this examine quarter.
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Extra info for Machine Learning Applications In Software Engineering (Series on Software Engineering and Knowledge Engineering)
ML methods used in prediction and estimation. NN IBL DT GA GP ILP EBL CL BL AL IAL RL EL SVM CBR Quality V Size V Development Cost Development Effort V Maintenance Effort -^ Resource Analysis V V V V V V V V V V \j \j yj Correction Cost \j y] yj Defects yj Reusability Release Time A/ yj Productivity -\j Execution Time Testability V V Software Cost Reliability V -\/ yj A primary concern in prediction or estimation models and methods is accuracy. There are some general issues about prediction accuracy.
Synthesizing Unix shell scripts from a high-level specification is made possible through IBL in . The tool has a retrieval mechanism that allows an appropriate source analog to be automatically retrieved given a description of a target problem. Several domain specific retrieval heuristics are utilized to estimate the closeness of two problems at implementation level based on their perceived closeness in the specification level. Though the prototype system demonstrates the viability of the approach, the scalability remains to be seen.
Their approach takes into consideration the following factors: number of clusters, similarity within a cluster and similarity among clusters. In , DT is used to model and predict the cost of rework in a library of reusable software components. Prescriptive coding rules can be generated from the model that can be used by programmers as guidelines to reduce the cost of rework in the future. The objective of the work is to use DT to help manage the maintenance of reusable components, and to improve the way the components are produced so as to reduce maintenance costs in the library.