Multi-action-based approach for constructing knowledge map

To alleviate the information overload in the product design process,this work proposes a multiaction-based method for constructing knowledge map. Since the relationships of knowledge are implicit in the collected user activities,the method calculates the similarity according to the collected user activities.Three concepts,including knowledge,action and user,are explained first. Based on this,the similarity calculation method is illustrated in detail. The dependencies of actions and relations of the user are considered in the calculation method. Further,the approach of applying the constructed knowledge map to alleviate information overload is proposed. At last,the proposed method is validated by a knowledge search and result comparison experiment.

User story clustering in agile development:a framework and an empirical study

Agile development aims at rapidly developing software while embracing the continuous evolution of user requirements along the whole development process.User stories are the primary means of requirements collection and elicitation in the agile development.A project can involve a large amount of user stories,which should be clustered into different groups based on their functionality’s similarity for systematic requirements analysis,effective mapping to developed features,and efficient maintenance.Nevertheless,the current user story clustering is mainly conducted in a manual manner,which is time-consuming and subjective to human bias.In this paper,we propose a novel approach for clustering the user stories automatically on the basis of natural language processing.Specifically,the sentence patterns of each component in a user story are first analysed and determined such that the critical structure in the representative tasks can be automatically extracted based on the user story meta-model.The similarity of user stories is calculated,which can be used to generate the connected graph as the basis of automatic user story clustering.We evaluate the approach based on thirteen datasets,compared against ten baseline techniques.Experimental results show that our clustering approach has higher accuracy,recall rate and F1-score than these baselines.It is demonstrated that the proposed approach can significantly improve the efficacy of user story clustering and thus enhance the overall performance of agile development.The study also highlights promising research directions for more accurate requirements elicitation.