首页|Automatic Requirement Dependency Extraction Based on Integrated Active Learning Strategies
Automatic Requirement Dependency Extraction Based on Integrated Active Learning Strategies
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Since requirement dependency extraction is a cognitively challenging and error-prone task,this paper proposes an automat-ic requirement dependency extraction method based on integrated active learning strategies.In this paper,the coefficient of variation method was used to determine the corresponding weight of the impact factors from three different angles:uncertainty probability,text similarity difference degree and active learning variant prediction divergence degree.By combining the three factors with the proposed calculation formula to measure the information value of dependency pairs,the top K dependency pairs with the highest comprehensive evaluation value are selected as the optimal samples.As the optimal samples are continuously added into the initial training set,the per-formance of the active learning model using different dependency features for requirement dependency extraction is rapidly improved.Therefore,compared with other active learning strategies,a higher evaluation measure of requirement dependency extraction can be achieved by using the same number of samples.Finally,the proposed method using the PV-DM dependency feature improves the weight-F1 by 2.71％,the weight-recall by 2.45％,and the weight-precision by 2.64％ in comparison with other strategies,saving approx-imately 46％ of the labelled data compared with the machine learning approach.
Requirement dependencydependency extractiondependency featuresintegrated active learning strategiescoefficient of variation
Hui Guan、Guorong Cai、Hang Xu
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Department of Computer Science and Technology,Shenyang University of Chemical Technology,Shenyang 110142,China
Key Laboratory of Industrial Intelligence Technology on Chemical Process,Shenyang 110142,China
Scientific Research Funding Project of Education Department of Liaoning Province 2021,China
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