Requirements-driven testing is crucial for validating functionality and maintaining software quality during the early stages of development.It is usually done manually by experienced experts, which is timeconsuming and thus affects development efficiency.Although a number of approaches have been proposed to automate the generation of test cases, they primarily rely on (semi-)formalized requirements and thus cannot be applied to user stories and other natural language-based requirements specifications that are more prevalent in practice.In this paper, we propose an incrementally refined multi-stage method that utilizes LLM to generate test cases from user stories automatically.Specifically, we design a systematic process to incrementally refine user stories and transform them into test cases in multiple stages.We compare the consistency, reasonable, testability, coverage, and time consumption of test cases generated by our method to those designed by experts and find that our proposal outperforms baseline methods in terms of all metrics, achieving 4.8 points testability and 86.36% coverage compared to the baseline's 4.5 points and 44.64%.This indicates a promising direction for improving the practicality and efficiency of test case generation from user stories.

The research of machine learning for requirements engineering (ML4RE) has attracted more and more attention from researchers and practitioners. Although pioneering research has shown the potential of using ML techniques to improve RE practices, there lacks a systematic and comprehensive literature review in academia that integrates an industrial perspective. Specifically, none of the reviews available in ML4RE have considered the grey literature, which is primarily from practitioner origin and is more reflective of the real issues and challenges faced in practice. In this paper, we conduct a systematic survey of academic publications in ML4RE and complement it with the practitioners’ voices from Stack Overflow to complete a comprehensive literature review. Our research objective is to provide a comprehensive view of the current research progress in ML4RE, present the main questions and challenges faced in RE practice, understand the gap between research and practice, and provide our insights into how the RE academic domain can pragmatically develop in the future. We systematically investigated 207 academic papers on ML4RE from 2010 to 2022, along with 375 questions related to RE practices on Stack Overflow and their corresponding answers. Our analysis encompassed their trends, focused RE activities and tasks, employed solutions, and associated data. Finally, we conducted a joint analysis, contrasting the outcomes of both parts. Based on the statistical results from collected literature, we summarize an academic roadmap and analyse the disparities, offering research recommendations. Our suggestions include the development of intelligent question-answering assistants employing large language models, the integration of machine learning into industrial tools, and the promotion of collaboration between academia and industry. This study contributes by providing a holistic view of ML4RE, delineating disparities between research and practice, and proposing pragmatic suggestions to bridge the academia-industry gap.

Goal modeling plays an imperative role in early requirements engineering, which has been investigated for decades. There have been many studies that show the usefulness of requirements goal models. However, the establishment of goal models is typically done manually, which is time-consuming and has a steep learning curve. In this paper, we propose a semi-automatic framework for constructing iStar models, which is a well-known goal modeling language. Specifically, we first investigate the practical needs of iStar modelers on the automation of iStar modeling by holding interviews, based on which we propose an interactive and iterative modeling process. Our proposal takes advantage of human decisions and artificial intelligence algorithms, respectively, aiming at achieving low modeling costs while maintaining the quality of models. We then propose a hybrid approach for automatically extracting goal model snippets from requirements text, which implements the automatic tasks of our proposed process. The proposed method combines logical reasoning with deep learning techniques so as to unleash the power of domain knowledge to assist with automation tasks. We have performed a series of experiments for evaluation. The experimental results show that our method achieves the F1-measure of 90.34% for actor entity extraction, 93.14% for intention entity extraction, and 83.18% for actor relation extraction, which can efficiently establish high-quality goal models. The artifacts are available at Zenodo1.

iStar modeling is beneficial in the early stage of requirements engineering, helping requirements analysts to analyze requirements and improve the efficiency and quality of the software development procedure. However, it is time-consuming and hard to learn to perform the iStar modeling manually, which can be more practical if the modeling process is automated.To facilitate the distribution of iStar practices, we designed a user-friendly semi-automatic iStar modeling approach to assist users in iStar modeling by extracting model elements from natural language requirement artifacts. Specifically, based on the analysis of the actual modeling process via interviewing, this work proposed an iStar modeling process, and automated three modeling steps: the actor entity extraction, the actor relation extraction, and the intention entity extraction. Then, this work proposes a hybrid method for natural language processing to extract the model elements in requirement sentences to automate the modeling steps. This hybrid method consists of two parts: the deep learning-based method and the logical reasoning method, which utilizes both methods simultaneously, ensuring the high accuracy of the results. Overall, this work proposed a user-friendly semi-automatic approach for aiding the iStar modeling, which proposes an iStar modeling process and automates many steps with hybrid natural language method during the process. We evaluated our proposed approach, and the results show that our proposed approach is efficient and helpful.

Abstract Sonar technology plays an important role in the development of marine resources and military strategy. Due to the bad quality of underwater acoustics channels, the sonar images collected by sonar technology equipment are easily affected by various kinds of distortions. To obtain high‐quality sonar images, the authors devise a novel dual‐path deep neural network (DPDNN) to measure the quality of sonar images. In these two paths, the authors use a batch normalization layer to reduce the training time and use the skip operation to speed up the feature extraction . Based on the above two operations, the authors extract the microscopic and macroscopic structures of sonar images, respectively. Finally, a global average pooling layer and a fully connection layer are used to connect the above two paths. Experiments show that the authors' DPDNN achieves significant improvements in prediction performance and efficiency. The source code will be published in the near future.

[Context] Establishing requirements models is an effective way to analyze them, which is typically dealt with in a graphical manner (i.e., the drag-and-draw fashion). However, as the size of models increases, the scalability issue has become an unignorable challenge, hindering the practical adoption of requirements modeling approach. Some researchers have recently proposed and promoted textual modeling approaches, mitigating these issues of requirements modeling. [Objective] In this paper, we aim at evaluating the two modeling methods, i.e., a graphical modeling method VS. a textual modeling method. In particular, we apply these two methods to iStar modeling language, which has been widely recognized as an effective means to model and analyze requirements. [Methods] We have systematically designed and conducted a controlled experiment with 38 participants to compare two iStar modeling methods (graphical and textual) using two corresponding modeling tools (piStar and T-Star). The experimental results reveal that the numbers of iStar model nodes and relationships built by the participants had no significant difference, regardless of the modeling method adopted. [Conclusions] First, the results show that the textual modeling method is as usable as the graphical modeling method when creating iStar models. Second, we have identified a number of issues that contribute to improving the utility and practicality of the iStar modeling method.