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Numerous academic research projects and industrial tasks related to software engineering require individual requirements as input. Unfortunately, according to our observation, several requirements may be packed in one paragraph without explicit boundaries in specification documents. To understand this problem's prevalence, we performed a preliminary study on the open requirement documents widely used in the academic community over the last 10 years, and found that 26% of them include this phenomenon. Several text segmentation approaches have been reported; however, they tend to identify topically coherent units which may contain more than one requirement. What is more, they do not take the constitutions of semantic units of requirements into consideration. Here we report a two‐phase learning‐based approach named DRIP to segment individual requirements from paragraphs. To be specific, we first propose a Requirement Segmentation Siamese framework, which models the similarity of sentences and their conjunction relations, and then detects the initial boundaries between individual requirements. Then, we optimize the boundaries heuristically based on the semantic completeness validation of the segments. Experiments with 1132 paragraphs and 6826 sentences show that DRIP outperforms the popular unsupervised and supervised text segmentation algorithms with respect to processing different documents (with accuracy gains of 57.65%–187.53%) and processing paragraphs of different complexity (with average accuracy gains of 54.46%–158.68%). We also show the importance of each component of DRIP to the segmentation. [ABSTRACT FROM AUTHOR]

Summary: Detecting missing requirements during software development is crucial to avoid unexpected consequences. However, this task is challenging due to limited domain knowledge of requirements analysts and the dynamic nature of software requirements. Previous studies have shown that requirement‐oriented domain models can help identify omissions in requirements, but they are often incomplete for many domains. Meanwhile, domain models constructed from other artifacts are available online. This raises the question: Can these domain models be useful in identifying missing functional information in requirement specifications? To address this question, we conducted a study to measure the overlap between entities in domain models and requirements. We analyzed the occurrence of overlapped entities, considering four distribution characteristics: the type of entities in the domain model, the distribution of mapped entities in the domain model, the family belonging of the mapped entities in the domain model, and the distribution of mapped entities in the requirements. Based on our findings, we proposed recommendations for missing requirements. Additionally, we performed experiments, including the use of the proposed metric "ancestors of the highest level with the most mapped entities" (AHME). The results showed significant improvements with F2$$ {F}_2 $$ gains of 146% and 223% in the two domains, highlighting the benefits of these distribution characteristics. [ABSTRACT FROM AUTHOR]

Context: The UML class diagram is commonly used to model functional structures and software code structures in both the preliminary and detailed design stages. And the abstraction level of UML class diagrams is usually higher than that of source code. Usually, there is a lack of trace links between these class diagrams and the source code, which may cause difficulties in understanding the source code, and affect the software evolution and maintenance. Objective: The main goal of this article is to establish the trace links between highly abstracted UML class diagrams in the design phase and source code, and eventually help practitioners better understand source code. Method: We propose an approach for the automated trace link establishment between UML class diagrams in the design phase and source code. To address the problem of abstraction level gap between them, we extend the UML class diagram by mining the synonymous phrases of class names and deducing the latent missing relationships between classes from multiple design documents. Then we build the trace links with a two‐phase approach including initial construction with fuzzy matching and further optimization by class relationship inference. Results: Experiments on five open‐source projects show that the recalls of our approach are over 94%, and the F2‐scores are over 88%, with the gains of 30% to 60% than the four baselines. Conclusion: Our work can be a reference for establishing the initial trace links between highly‐abstracted UML class diagrams and source code. Towards the higher abstraction of design diagrams, we extend UML class diagrams with the statistical analysis on multiple design documents. To guarantee the quality of trace links, we design a two‐phase approach by obtaining the "full but not good enough" trace links and filtering the "probably wrong" links. Experiments show that the main techniques of our approach behave as important role for tracing between high‐level class diagrams and source code. [ABSTRACT FROM AUTHOR]

Privacy policies are critical for helping individuals make decisions on the usage of information systems. However, as a common language phenomenon, ambiguity occurs pervasively in privacy policies and largely impedes their usefulness. The existing research focuses on the identification of individual vague words or sentences, without considering the context of documents, which may cause a significant amount of false vagueness. Our goal is to automatically detect the potential false vagueness and the related supporting evidence, which illustrates or explains the vagueness, and therefore probably assist in alleviating the vagueness. We firstly analyze the public manual annotations and define four common patterns of false vagueness and three types of supporting evidence. Then we propose the approach of the F·vague-Detector to automatically detect the supporting evidence and then locate the corresponding potential false vagueness. According to our analysis, about 29–39% of individual vague sentences have at least one clarifying sentence in the documents, and experiments show good performance of our approach, with recall of 66.98–67.95%, precision of 70.59–94.85%, and F 1 of 69.24–78.51% on the potential false vagueness detection. Detecting the vagueness of isolated sentences without considering their context within the whole document would bring about one-third potential false vagueness, and our approach can detect this potential false vagueness and the alleviating evidence effectively. [ABSTRACT FROM AUTHOR]

Cross-level requirement trace links (i.e., links between high-level requirements (HLRs) and low-level requirements (LLRs)) record the top-down decomposition process of requirements and support various development and management activities (e.g., requirement validation). Undoubtedly, updating trace links synchronously with requirement changes is critical for their constant availability. However, large-scale open-source software that is rapidly iterative and continually released has numerous requirements that are dynamic. These requirements render timely update of trace links challenging. To address these problems, in this study, a novel deep-learning-based method, deep requirement trace analyzer fusing heterogeneous features (DRAFT), was proposed for updating trace links between various levels of requirements. Considering both the semantic information of requirement text descriptions and the process features based on metadata, trace link data accumulated in the early stage are comprehensively used to train the trace link identification model. Particularly, first, we performed second-phase pre-training for the bidirectional encoder representations from transformers (BERT) language model based on the project document corpus to realize project-related knowledge transfer, which yields superior text embedding. Second, we designed 11 heuristic features based on the requirement metadata in the open-source system. Based on these features and semantic similarity between HLRs and LLRs, we designed a cross-level requirement tracing model for new requirements. The superiority of DRAFT was verified based on the requirement datasets of eight open-source projects. The average F1 and F2 scores of DRAFT were 69.3% and 76.9%, respectively, which were 16.5% and 22.3% higher than baselines. An ablation experiment proved the positive role of two key steps in trace link construction. [ABSTRACT FROM AUTHOR]

Software requirements specification is undoubtedly critical for the whole software life-cycle. Currently, writing software requirements specifications primarily depends on human work. Although massive studies have been proposed to speed up the process via proposing advanced elicitation and analysis techniques, it is still a time-consuming and error-prone task, which needs to take domain knowledge and business information into consideration. In this paper, we propose an approach, named ReqGen, which can provide further assistance by automatically generating natural language requirements specifications based on certain given keywords. Specifically, ReqGen consists of three critical steps. First, keywords-oriented knowledge is selected from the domain ontology and is injected into the basic Unified pre-trained Language Model (UniLM) for domain fine-tuning. Second, a copy mechanism is integrated to ensure the occurrence of keywords in the generated statements. Finally, a requirements-syntax-constrained decoding is designed to close the semantic and syntax distance between the candidate and reference specifications. Experiments on two public datasets from different groups and domains show that ReqGen outperforms six popular natural language generation approaches with respect to the hard constraint of keywords' (phrases') inclusion, BLEU, ROUGE, and syntax compliance. We believe that ReqGen can promote the efficiency and intelligence of specifying software requirements. [ABSTRACT FROM AUTHOR]

This study aimed at evaluate the effects of different aperture-sized type I collagen/silk fibroin (CSF) scaffolds on the proliferation and differentiation of human dental pulp cells (HDPCs). The CSF scaffolds were designed with 3D mapping software Solidworks. Three different aperture-sized scaffolds (CSF1–CSF3) were prepared by low-temperature deposition 3D printing technology. The morphology was observed by scanning electron microscope (SEM) and optical coherence tomography. The porosity, hydrophilicity and mechanical capacity of the scaffold were detected, respectively. HDPCs (third passage, 1 × 105 cells) were seeded into each scaffold and investigated by SEM, CCK-8, alkaline phosphatase (ALP) activity and HE staining. The CSF scaffolds had porous structures with macropores and micropores. The macropore size of CSF1 to CSF3 was 421 ± 27 μm, 579 ± 36 μm and 707 ± 43 μm, respectively. The porosity was 69.8 ± 2.2%, 80.1 ± 2.8% and 86.5 ± 3.3%, respectively. All these scaffolds enhanced the adhesion and proliferation of HDPCs. The ALP activity in the CSF1 group was higher than that in the CSF3 groups (P  <   0.01). HE staining showed HDPCs grew in multilayer within the scaffolds. CSF scaffolds significantly improved the adhesion and ALP activity of HDPCs. CSF scaffolds were promising candidates in dentine-pulp complex regeneration. [ABSTRACT FROM AUTHOR]

The microservice application programming interface (API) becomes a growing concern in the IT industry, as a result of the increasing usage of microservice architecture style. There exist many successful practices among companies, communities, and so on. In contrast, the related academic research is still at an early stage, where lacks an overview of technologies for the design, implementation and operation of microservice APIs, as well as a general picture of concerns. In this article, we try to fill this gap by eliciting the technologies and concerns on microservice APIs and establishing a microservice API description model, with the intention of aiding researchers to gain an overview of this field and find possible research directions, and helping practitioners to better understand microservice APIs and be aware of the existing approaches for daily work. Twelve academic papers and 38 gray literatures are selected and analyzed following the systematic literature review approach. Besides, we give our observations from this study. For researchers, our findings show the most cared concerns of practitioners, and our description model can be used as a reference for new theories, experiments, and future research dimensions. For practitioners, our study can be used as a guideline for microservices experimentation and a starting point for practice. [ABSTRACT FROM AUTHOR]

The overwhelming foreground contamination hinders the accurate detection of the 21-cm signal of neutral hydrogen during the Epoch of Reionization (EoR). Among various foreground components, the Galactic free–free emission is less studied, so that its impact on the EoR observations remains unclear. In this work, we employ the observed H α intensity map with the correction of dust absorption and scattering, the simfast21 software, and the latest SKA1-Low layout configuration to simulate the SKA 'observed' images of Galactic free–free emission and the EoR signal. By calculating the 1D power spectra from the simulated image cubes, we find that the Galactic free–free emission is about 103.5–102.0, 103.0–101.3, and 102.5–101.0 times more luminous than the EoR signal on scales of 0.1 Mpc−1 < k < 2 Mpc−1 in the 116–124, 146–154, and 186–194 MHz frequency bands. We further analyse the 2D power spectra inside the properly defined EoR window and find that the leaked Galactic free–free emission can still cause non-negligible contamination, as the ratios of its power (amplitude squared) to the EoR signal power can reach about 200, 60, and 15 per cent on scales of k ∼ 1.2 Mpc−1 in three frequency bands, respectively. Therefore, we conclude that the Galactic free–free emission, as a severe contaminating foreground component, needs to be carefully treated in the forthcoming deep EoR observations. [ABSTRACT FROM AUTHOR]

Objective. To investigate the frequency and influencing factors of rubber dam usage for endodontic procedures among general dentistry practitioners and specialized practitioners (endodontist) in Tianjin. Methods. Three hundred questionnaires were distributed among practitioners from 3 different types of medical institutions in Tianjin. Data were collected and analysed using Chi-square tests. Results. There were 63.3% of respondents who have used rubber dam (response rate 82.7%, valid response rate 76.3%). However, only 0.4% and 3.1% of them recognized using rubber dam “every time” during caries direct restoration and root canal therapy, respectively. There was no significant difference in rubber dam usage between male and female practitioners. Among the respondents, practitioners with working experience between 5 and 10 years showed the highest usage rate (76.3%), while practitioners working more than 20 years showed the lowest (53.2%). The endodontists gained the highest and the most frequent usage rate and the best rubber dam technique mastering skills. Practitioners working in those stomatological departments of general hospitals showed the lowest rubber dam usage rate. Conclusions. The prevalence of rubber dam usage in Tianjin city is still low. The practitioner’s gender, years of professional experience, general or specialized field, and the type of dental setting they work for are the factors that need to be considered during making policy and executing training. [ABSTRACT FROM AUTHOR]

Line emissions of high- and low-density molecular tracers serve as powerful diagnostic tools for the ISM in both Galactic and extragalactic star formation environments. The emission line strengths and line ratios may be interpreted using detailed modeling of both the dominant physics and the chemistry of the molecular constituents. Observed molecular line ratios will thus reveal the signatures of dominant UV, X-ray, and CR radiation fields and of mechanical heating and feedback from the star formation process. In addition, certain line ratios reflect the physical and chemical changes resulting from the time-evolution of a star formation region. In this paper, we present results of Galactic sources and extragalactic starbursts covering a large range of FIR luminosities and illustrate the similarities between the diagnostics of these environments. [ABSTRACT FROM PUBLISHER]

Nanocomposites of octavinyl polyhedral oligomeric silsesquioxane (OVPOSS) and functionalized SiO2 were investigated in order to determine the effect of particles on the morphology and mechanical properties of PMMA. The outcome of the study suggested that functionalized SiO2 and octavinylPOSS molecule had different morphology. As proved by X-ray diffraction and transmission electron microscopy analysis, the crystal structure of OVPOSS molecule was significantly different from amorphous aggregates of functionalized SiO2. With the additional particles in the nanocomposites, the sizes of octavinylPOSS and functionalized SiO2 began to reduce. This illustrated that the separation of aggregates led to the formation of irregular POSS molecules and amorphous SiO2 particles varied. Differential scanning calorimetry analysis indicated that PMMA-POSS nanocomposites had a homogeneous system. However, there was a significant phase separation at 3 wt.% SiO2. PMMA-SiO2 nanocomposites displayed lower reinforcing effects than expected, based on the mechanical properties of nanocomposites containing OVPOSS molecules. [ABSTRACT FROM PUBLISHER]

Background: Current cariology education based on the traditional teaching method faces a lot of challenges. Meanwhile, the COVID-19 pandemic caused an unprecedented disruption in medical education and health care systems worldwide. Innovation in the teaching mode of cariology education is required to change the situation.Objective: The goal of the research was to evaluate the application effects of a case-based social media-assisted teaching method in cariology education.Methods: Dental students of class 2019 were enrolled into the experimental group, while students of class 2018 served as control. A case-based social media-assisted teaching method was used in the experimental group, which included preclass activity via social media, additional discussion and practice process record in class, and questions and answers on the platform after class. The traditional teaching method, which consisted of conventional preparation before class, traditional lectures and demonstrations followed by students practice in class, and questions and answers step after class, was used in the control group. The teaching materials were the same in both groups. At the end of the program, students from both groups took cavity preparation skill evaluation tests. Questionnaires were tested on the case-based social media-assisted teaching group students anonymously. All data were analyzed using SPSS statistical software (version 22.0, IBM Corp).Results: The mean student cavity preparation skill evaluation scores was 82.51 (SD 6.82) in the experimental group and 77.19 (SD 5.98) in the control group (P<.05). The questionnaire response rate was 100%. Of those, 94.3% (100/106) of the students recommended the case-based social media-assisted teaching method in cariology education. The majority of the participants agreed that it helped them memorize the theoretical knowledge of cariology, facilitated in-depth discussion, improved their enthusiasm and initiative in learning, and enhanced the relationship between teachers and students (104/106, 98.1%). They also recognized that the classroom atmosphere was active (94/106, 88.7%).Conclusions: The case-based social media-assisted teaching method was beneficial in terms of learning, as demonstrated by the statistically significant improvement of the cavity preparation skill evaluation scores and satisfaction from attending students. This method could be used to supplement the teaching of cariology. [ABSTRACT FROM AUTHOR]

Tooth developmental stage evaluation is important in dental and chronological age estimation, and it is important for accurate diagnoses and appropriate treatment in dental practice. It is routinely assessed by clinical observations and radiographic techniques. This study aimed at ascertaining tooth developmental stage judgments made by examiners and Mimics software according to the Nolla method with radiographs. Meanwhile, the true tooth developmental stages would be explored with histological analysis. Twenty freshly slaughtered porcine heads were collected and hemisected, and both the left and right mandibular samples were numbered. The developmental stages of the second and third permanent molars (M2 and M3) were evaluated by examiners and Mimics software analysis. The ratio of the radiopaque calcified area to the dental follicle (RCA/DF) at different stages was calculated. Both non-decalcified and decalcified samples were processed for histologic observation. The results showed significant differences between RCA/DF ratios from different developmental stages. There was a high positive correlation between the examiners' evaluation results and Mimics analysis results. Radiograph judgments and histology observation results were consistent from Stages 2-6. However, radiograph images of Stage 1 samples showed only crypts present, while under a surgical operating microscope, a bell-shaped tooth germ was observed. This was also confirmed by normal and hard tissue histology. In conclusion, radiograph judgments made by either examiners or Mimics software were both reliable. Mimics analysis can be a useful tool in evaluating tooth developmental stages. However, judgments need to be made cautiously in early developmental stages., (© 2020 American Association for Anatomy.)