ISSN 1000-1239 CN 11-1777/TP

    Requirements Engineering Under the Background of Artificial Intelligenc

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    Requirements Engineering Under the Background of Artificial Intelligence Preface#br#
    Liu Lin, Li Zhi
    Journal of Computer Research and Development    2021, 58 (4): 681-682.   DOI: 10.7544/issn1000-1239.2021.qy0401
    Abstract831)   HTML384)    PDF (220KB)(585)       Save
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    Intelligent Requirements Elicitation and Modeling: A Literature Review
    Wang Ye, Chen Junwu, Xia Xin, Jiang Bo
    Journal of Computer Research and Development    2021, 58 (4): 683-705.   DOI: 10.7544/issn1000-1239.2021.20200740
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    Requirements elicitation and modeling refer to the process of obtaining explicit or implicit requirements from the requirements text described in natural language, and constructing the corresponding models through tabular, graphical, and formulaic methods. Requirements elicitation and modeling is an extremely critical step in software development process, which paves the way for subsequent system design and implementation, improves the efficiency and quality of software development, and improves the stability and feasibility of software systems. Researchers have obtained a series of research achievements in requirements elicitation and modeling. Requirements elicitation and modeling can be generally divided into three steps: requirements knowledge extraction, requirements knowledge classification and requirements model construction. Due to the fact that traditional requirements elicitation and modeling approaches have problems in terms of accuracy and efficiency of model construction, in recent years, more and more researchers have integrated widely applicable artificial intelligence techniques with these approaches, and put forward a series of intelligent require-ments elicitation and modeling approaches, so as to make up for the deficiencies of the traditional methods. This paper focuses on the perspective of intelligent requirements elicitation and modeling, and sorts out and summarizes the research progress of requirements elicitation and modeling in recent years. The main contents include: 1)statistics and analysis of the artificial intelligence techniques applied in requirements knowledge extraction, requirements knowledge classification and requirements model construction; 2)summarizing the verification and evaluation methods used in the process of intelligent requirements elicitation and modeling; 3)summarizing the key issues of intelligent require-ments elicitation and modeling from two aspects of scientific problems and technical difficulties, and elaborating on the six research trends including integrated and dynamic model construction, mining the relationships among intelligent requirements elicitation and modeling and other software engineering activities, refining the granularity of intelligent requirements modeling, data sets construction, evaluation metrics construction and industrial practice as the possible solutions to the above problems. The future development trend of intelligent requirements elicitation and modeling research is also discussed.
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    An Automated Approach to Generate SysML Models from Restricted Natural Language Requirements in Chinese
    Bao Yang, Yang Zhibin, Yang Yongqiang, Xie Jian, Zhou Yong, Yue Tao, Huang Zhiqiu, Guo Peng
    Journal of Computer Research and Development    2021, 58 (4): 706-730.   DOI: 10.7544/issn1000-1239.2021.20200757
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    Model-driven development has been gradually adopted as an important approach of designing and developing safety-critical cyber-physical systems(SC-CPSs). The requirement of SC-CPSs is often described in natural language. How to link natural language requirements and the model-driven design and development process of SC-CPSs automatically or semi-automatically is a main existing challenge. In this paper, a method named RNL2SysML is proposed for the automatic generation of SysML models from restricted natural language requirements in Chinese. Firstly, in view of the problem that glossaries need to be manually extracted, a method for extracting and recommending terms of SC-CPSs based on artificial intelligence is proposed. Secondly, in order to reduce the ambiguity of natural language requirements, a restricted natural language requirement template is proposed for requirement specification. Then, the method of transformation from natural language requirement specification to SysML model is given. Finally, based on the open source tool Papyrus, the plugin for the method proposed in this paper is implemented, and the effectiveness and practicality of the method is evaluated and proved by an industry case of the airplane air compressor system in the aviation field.
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    An Approach for Improving the Requirements Quality of User Stories
    Wang Chunhui, Jin Zhi, Zhao Haiyan, Cui Muyuan
    Journal of Computer Research and Development    2021, 58 (4): 731-748.   DOI: 10.7544/issn1000-1239.2021.20200732
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    User story is a widely adopted requirements notation in agile development. Generally, user stories are written by customers or users in natural language with limited format, but there are often some defects in the writing of user stories. The typical detects include the lack of necessary information to make it difficult to understand, and the ambiguous expressions make the requirements impossible to estimate, and some stories have duplicates and conflicts. These defects affect the quality of requirements, resulting in incomplete, inconsistent, untestable, and so on. This paper proposes an automated approach for detecting the defects in user story requirements and improving the quality of user stories. First, a conceptual model of user story for defect identification is proposed. An approach based on structural analysis, syntactic analysis and semantic analysis is used for constructing the conceptual model. Secondly, 11 quality criteria are summarized from the actual cases and used to identify the defects in the user stories. An experimental study is carried out on a story set with 36 user stories and 84 scenarios. The automatic detection tool reports 173 defects, and the precision and recall of the reported results are 88.79% and 95.06%, respectively.
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    A User Requirements Preference Analysis Method of Mobile Applications Based on Meta-Path Embedding
    Song Rui, LiTong, Dong Xin, Ding Zhiming
    Journal of Computer Research and Development    2021, 58 (4): 749-762.   DOI: 10.7544/issn1000-1239.2021.20200737
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    With the rapid development of the Internet and mobile application platforms, massive user data has been generated by mobile applications. Such data has become an important data source for accurately analyzing user requirements preference. Many researchers have analyzed and mined user requirements preference from user data. However, the existing studies do not link the multi-dimensional information of mobile applications, and only explore the characteristics of a few dimensions of the data. In this paper, we propose a method to analyze user requirements preferences based on meta-path embedding, which can personally recommend mobile applications for users. Specifically, we first analyze the semantic topics in the text information of mobile applications, which enriches the analysis dimension of user requirements preferences. Second, we construct a conceptual model that integrates multi-dimensional information for mobile applications, including multi-dimensional data that affects user choices. Based on the conceptual model, we design a series of meaningful meta-paths to accurately capture the semantics of user requirements preferences. Finally, we analyze user preferences based on the meta-path embedding technique to recommend personalized mobile applications for users. In this paper, we use the real data set obtained from the Apple App Store to evaluate our model, which contains 1507 mobile applications and 153501 user reviews. The experimental results show that our method outperforms the existing models in all metrics, in which the average F1-measure increases by 0.02, and the average NDCG increases by 0.1.
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    Automatic Trend Analysis of Mobile App Updates Based on App Changelogs
    Zhong Renyi, WangChong, Liang Peng, Luo Zhong
    Journal of Computer Research and Development    2021, 58 (4): 763-776.   DOI: 10.7544/issn1000-1239.2021.20200756
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    Data-driven analysis on the development, maintenance, and evolution has recently become an area of active research. However, little is known to treat app changelogs as the input to explore the types of requirements that app developers pay the most attention when releasing an app, as well as trend of app development and updates. This paper reports the results of an exploratory study in which we analyze the requirements and buzzwords that dominate the changes of apps, according to a set of 6527 changes collected from 60 apps from three categories in the Apple App Store: “Travel”, “Social Networking” and “Books”. First, the performance of three supervised machine learning algorithms is evaluated to find the most suitable classifiers for the automatic classification of app changelogs. Furthermore, based on the classification results of app changelogs, characteristics and trends of app updates are revealed from two perspectives, i.e., the requirement type that app changelog items mention and the hot words in app changelog items that are labeled as a certain requirement type. The results are valuable for researchers and practitioners to have a comprehensive understanding on the current app stores from RE perspective.
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    Enhancing Requirements Traceability Recovery via a Graph Mining-Based Expansion Learning
    Chen Lei, Wang Dandan, Wang Qing, Shi Lin
    Journal of Computer Research and Development    2021, 58 (4): 777-793.   DOI: 10.7544/issn1000-1239.2021.20200733
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    Requirements tracing plays an important role to manage requirements and its related artifacts through the entire software life cycle. As manually creating such trace links is time-consuming and error-prone, some information retrieval (IR) based and machine learning (ML) based solutions have been proposed. Among them, unsupervised ML methods which do not require large labeled datasets are gaining more attention. Most of these solutions model the lexical and semantic information to resolve the problem. However, we find that existing approaches typically neglect the word co-occurrence distribution and word order information of the textual artifacts, which could provide extra indications for enhancing trace links. In this paper, we propose a novel approach, named GeT2Trace, which utilizes a graph mining-based expansion learning to enhance trace links recovery. The key idea is to exploit the word co-occurrence information and the word order information via graph network, and leverage them to learn a more comprehensive and accurate artifact representation. Evaluation is conducted on five public datasets, and the results show that our approach outperforms the state-of-the-art baselines. Expanding requirements with graphic information provide new insights into the unsupervised traceability solutions, and the improved trace links confirm the usefulness and effectiveness of GeT2Trace.
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