Reviewerly/exHarmony huggingface dataset:
Development of Class Diagrams Based on Use Case, and Sequence Diagrams Using a Text Mining Approach in SRS Penguin

Software requirement specification is a document that can be used as a guide for developers to develop applications. This study uses SRS from the Penguin application to help determine the development of class diagrams based on use case and sequence diagrams using the text mining method. The results of this process will be calculated for similarity, after which validation and testing will be carried out using Gwet’s AC1 and Cohen Kappa. Based on the results and discussion, three artifacts were formed, namely actors from use case diagrams (AUC), objects from sequence diagrams (OSD), and class names from class diagrams (NCD). The three artifacts produce two comparisons in the formation of class diagrams. The first comparison is between AUC and NCD, with the highest cosine similarity score of 0.666. From this score, the resulting construction of class diagram component names is seller and customer. The first comparison also resulted in a score of 0.088 for Cohen Kappa and 0.756 for Gwet’s AC1. Furthermore, for the second comparison, between OSD and NCD, two results were obtained with the same score, namely 0.9. This score resulted in the formation of class component names such as seller, transaction page, revenue page, expenditure page, and penguin app system. And the second comparison has a Cohen kappa score of 0.112 and 0.926 for Gwet’s AC1 score. The results of the Cohen Kappa score, and Gwet’s AC1 can be used as recommendations for improving class names that match the actors names in use case diagram, and object names in the sequence diagram.
AWFCNET: An Attention-Aware Deep Learning Network with Fusion Classifier for Breast Cancer Classification Using Enhanced Mammograms

Breast cancer remains a significant public health concern and a leading cause of female mortality despite recent advances in healthcare. Experts agree that its early prognosis is a key to survivability. In this research, we proposed a deep learning architecture code-named AWFCNET. It comprised multiple segments of preprocessing techniques (color shifting & image enhancement), feature learning based on ResNeXt-101 convolutional network as a backbone with transfer and attention-aware mechanisms, and fusion classifier composed of three recurrent neural networks. The generalization capability of the pipeline produced 98.10% accuracy on a mammogram dataset using 10-fold cross-validation. Computational benchmarks revealed that it surpassed existing state-of-the-art approaches with provisions of visual interpretability via gradient maps. Thus, our framework could complement physicians’ expertise in rapid and dependable breast cancer diagnoses.
Text Data Processing on Non-Functional Requirement for the Similarity Between Requirement Elicitation with Deployment Diagram and Recommendation for SRS Improvement

The discrepancy between the developer’s understanding of the client’s wishes in the Software Requirement Specification document is one of the backgrounds in this research. This research uses SRS Baker’s Corner, which is an Android-based cake and pastry product ordering platform. The primary purpose of this research is to process text data on Non-Functional Requirements, where the extraction results can be used as a reference for comparing the suitability between Requirement Elicitation and Deployment Diagram with the text pre-processing method. This research has produced two similarity values, namely between Requirement Elicitation with Non-Functional Requirement and Non-Functional Requirement with Deployment Diagram based on the results of text data extraction in d1-d15. The naming of d1 to d15 is an identity for a text document. The value "d" stands for "document." In the search process for Requirement Elicitation with Non-Functional Requirement, the cosine similarity value closest to 1 is 0.751304, obtained from d2 and d6. Based on the cosine similarity value, the kappa index value is 0.54488 or has a moderate proportion of agreement, and Gwet’s AC1 value is 0.88041, which has an almost perfect proportion of agreement. In the search process for Non-Functional Requirement with Deployment Diagram, the cosine similarity value, which is relatively high, is 0.604605, which is obtained from d6 and d11. Based on the cosine similarity value, the kappa index value is 0.30845 or has a fair proportion of agreement, and Gwet’s AC1 value is 0.95837, which has an almost perfect proportion of agreement. In addition to producing similarity values, there are improvement recommendations for documents with relatively low kappa index values.
Domain-Knowledge Enhanced Machine Learning for Table Tennis Stroke Characterization

Machine learning in wearable applications characterizing body movements is particularly challenging due to constraints in energy and computation, as well as due to a large space of possible usage scenarios. In this work, we are pushing these boundaries with a focus on table tennis stroke recognition and present a novel domain-specific approach at various abstraction levels, ranging from low-level signal corrections, context-dependent sensor fusion, stroke type characteristics, to a valid gameplay state machine. Our implementation runs entirely on a player’s smartwatch and shows that ML specialization is critical to achieving robustness for practical use. Our pipeline and classifier cascade is based on LSTM and successfully distinguishes between strokes and non-stroke actions, as well as eight types of strokes (drive, loop, block, and push in forehand and backhand). Training and validation were controlled in an environment where a table tennis robot served different players thousands of precisely controlled shots that required specific stroke responses. The final ML classifier pipeline is only 82.5 KB in size, achieves a maximum F1-score of 97.27 % for stroke classification and a maximum F1-score of 91.75 % in distinguishing strokes from non-stroke actions. Another novel feature is that our stroke type classifiers can self-correct based on the gameplay model that excludes humanly impossible transitions of moves.
Use of Chatbots in E-Learning Context: A Systematic Review

The integration of Chatbot systems in the education sector has been made possible due to advancements in Artificial Intelligence (AI) technology. The adoption of this technology for educational purposes is progressively on the rise. Chatbots have the ability to support users with faster and more personalized service. The expanding use of e-learning emphasizes the importance of studying this topic more deeply. A comprehensive review of current studies on the use of chatbots in education, especially in the field of e-learning, was conducted. Using a systematic review methodology, 20 relevant articles obtained from a reputable digital database were analyzed. The findings of this review contribute to an enhanced comprehension of previous studies efforts concerning the utilization of Chatbots in e-learning development. These insights include the scope of the research, the benefits and obstacles encountered, and potential areas for further study.
Deep Reinforcement Learning Based Task-Oriented Communication in Multi-Agent Systems

Driven by the increasing demand for executing intelligent tasks in various fields, multi-agent system (MAS) has drawn significant attention recently. An MAS relies on efficient communication between agents to exchange task-relevant information, so as support cooperative operation. Meanwhile, traditional communication systems are bit-oriented, which neglect the content and task relevance of the transmitted data. Thus, if bit-oriented communication patterns are applied in a MAS, a significant amount of task-irrelevant data would be transmitted, leading to communication resource waste and low operational efficiency. Considering that many emerging MASs are data-intensive and delay-sensitive, traditional ways of communication are unfit for these MASs. Task-oriented communication is a promising solution to deal with this issue, but its application in MAS still faces various challenges. In this article, we propose a task-oriented communication based framework for MAS, aiming to support efficient cooperation among agents. This framework specifies the collection, transmission, and processing of task-relevant information, in which task relevance is fully utilized to enhance communication efficiency. Based on the proposed framework, we then apply deep reinforcement learning (DRL) to implement task-oriented communication, in which a modular design and an end-to-end design for information extraction, data transmission, and task execution are proposed. Finally, the open problems for future research are discussed.
Data Utilization Versus Privacy Protection in Semantic Communications

Semantic communication is considered as a promising enabler for saving gradually exhausted spectrum resources due to its innovative idea of aggressively extracting critical information. Although the learnable semantic-aware codec brings exciting compression gains, its data-driven kernel endogenously incurs conflicts between data utilization and privacy protection. Namely, the data utility loss hurts transmission reliability while the need toward knowledge sharing brings privacy disclosure concerns. In this article, we first analyze the key challenges of semantic communication derived from its high dependence on sample richness and diversity. To strike an efficient trade-off between data utilization and privacy protection, we propose a joint online inference and offline training framework for semantic communication, which covers privacy-protected codec training and personalized codec deployment. Specifically, a novel distributed learning strategy is developed for codec training, where model splitting between clients and servers makes gradient leakage attacks computationally prohibitive and partial model aggregation provides customized data utilization. For personalized codec deployment, a model pre-training and distillation scheme is designed by exploiting modality-aware semantic associations. Lastly, an experimental case study demonstrates the superiority of our proposed strategy in balancing data utilization and privacy protection in semantic communications.
Adaptive Lazily Aggregation Based on Error Accumulation

Federated Learning (FL) enables multiple clients to collaboratively train models without exposing their local data. FL is an effective approach to utilizing localized data while preserving clients’ data privacy, but it also brings significant communication overhead. To reduce communication overhead of FL, this paper proposes the Adaptive Lazily Aggregation based on Error Accumulation (EA-ALA) algorithm. It uses adaptive constraints to determine whether a client can skip a communication round with the server so as to diminish communication cost. It also adopts error accumulation to improve model accuracy. The experimental results on CIFAR10 and Fashion-MNIST datasets show that compared to vanilla FL, EA-ALA consumes only 52% and 61% of communication rounds to achieve higher model accuracy.
Assessing Driver Gaze Location in a Dynamic Vehicle Environment

Measuring the human gaze is an important area of research due to this measurement's ability to give insight into what or where a person is focused and/or paying attention to. However, gaze has been very challenging to measure effectively and then convert into a metric. The problem of measuring human gaze is challenging in the context of dynamic environments with motion of the subject or the environment itself. One domain of research that has sought these gaze-related attention metrics has been the area of automotive driver assessment. Being able to understand if a driver is looking at relevant areas as well as scanning the road for hazards is a valuable metric to evaluate if an individual is fit to drive. Eye-tracking glasses measure where a person is looking relative to their head position but do not map this information against important regions within the visual field. This paper provides a computationally scalable method to identify relevant regions within a dynamic visual field and allow for the measurement of what a driver is focused on, reducing the need for extensive manual segmentation. The paper provides a method of identifying the windshield and other key regions within a motor vehicle typical for a driver's field of view. The identification of key regions was accomplished through the application of convolutional neural networks (CNNs) with a Dice score of 0.9404 The model is then shown to allow for the assessment of visual focus for drivers.
Density Peak Clustering Based on Global Density

Density peaks clustering is an efficient clustering method. It regards density peaks determined by local density which depends on cutoff distance as cluster centers to recognize clusters. However, it is difficult to find the appropriate cutoff distance to accurately measure local density. In this paper, we define global density to solve above problem. In order to define the global density of a point, we define the density between any two points. The global density of a point is defined as the sum of the densities from a point to other points. We apply our global density to the density peaks clustering and then propose a new clustering method. Experiments illustrate the effectiveness of our new method.
Investigating the Generalizability of Deep Learning-based Clone Detectors

The generalizability of Deep Learning (DL) models is a significant challenge, as poor generalizability indicates that the model has overfitted to the training data and is not able to generalize to new data. Despite numerous DL-based clone detectors emerging in recent years, their generalizability has not been thoroughly assessed. This study investigates the generalizability of three DL-based clone detectors (CCLearner, ASTNN, and CodeBERT) by comparing their detection accuracy on different training and testing clone benchmarks. The results show that all three clone detectors do not generalize well to new data and there is a strong relationship between clone types and generalizability for CCLearner and ASTNN.
Evaluating a Language Workbench: from Working Memory Capacity to Comprehension to Acceptance

Language workbenches are tools that enable the definition, reuse and composition of programming languages and their ecosystem. This breed of frameworks aims to make the development of new languages easier and more affordable. Consequently, the comprehensibility of the language used in a language workbench (i.e., the meta-language) should be an important aspect to consider and evaluate. To the best of our knowledge, although the quantitative aspects of language workbenches are often discussed in the literature, the evaluation of comprehensibility is typically neglected.Neverlang is a language workbench that enables the definition of languages with a modular approach. This paper presents a preliminary study that intends to assess the comprehensibility of Neverlang programs, evaluated in terms of users’ effectiveness and efficiency in a code comprehension task. The study also investigates the relationship between Neverlang comprehensibility and the users’ working memory capacity. Furthermore, we intend to capture the relationship between Neverlang comprehensibility and users’ acceptance, in terms of perceived ease of use, perceived usefulness, and intention to use. Our preliminary results on 10 subjects suggest that the users’ working memory capacity may be related to the ability to comprehend Neverlang programs. On the other hand, effectiveness and efficiency do not appear to be associated with an increase in users’ acceptance variables.
Performance Prediction From Source Code Is Task and Domain Specific

Performance is key to the success and adoption of software systems. In video games, performance is commonly highlighted as one of the top quality concerns raised by players. To check the performance of their systems, development teams tend to rely on profiling and monitoring tools, which observe program executions to identify regressions. The usage of static analysis tools for this purpose has been so far limited. Lately, the success of Large Language Models in many code analytics tools led to attempts to leverage them in static performance analysis. These studies showed promising results in predicting runtime and regressions on large public datasets. In this paper, we evaluate the usability of such models in practice, and particularly in the domain of video games. We train a state-of-the-art neural network on the Code4Bench dataset to predict runtime regressions for programming competition programs, then evaluate its ability to generalize to new domains. Our results show that these models achieve great results (e.g. 95.73% accuracy for performance comparison) on the original domain for programs solving in-sample programming tasks, yet fail to generalize to out-of-sample tasks. Furthermore, we show that transfer techniques such as domain adversarial adaptation and model fine-tuning are not sufficient to transfer these models to the target industrial domain of AAA games.
Comparing 2D and Augmented Reality Visualizations for Microservice System Understandability: A Controlled Experiment

Microservice-based systems are often complex to understand, especially when their sizes grow. Abstracted views help practitioners with the system understanding from a certain perspective. Recent advancement in interactive data visualization begs the question of whether established software engineering models to visualize system design remain the most suited approach for the service-oriented design of microservices. Our recent work proposed presenting a 3D visualization for microservices in augmented reality. This paper analyzes whether such an approach brings any benefits to practitioners when dealing with selected architectural questions related to system design quality. For this purpose, we conducted a controlled experiment involving 20 participants investigating their performance in identifying service dependency, service cardinality, and bottlenecks. Results show that the 3D enables novices to perform as well as experts in the detection of service dependencies, especially in large systems, while no differences are reported for the identification of service cardinality and bottlenecks. We recommend industry and researchers to further investigate AR for microservice architectural analysis, especially to ease the onboarding of new developers in microservice projects.
Generation-based Code Review Automation: How Far Are Weƒ

Code review is an effective software quality assurance activity; however, it is labor-intensive and time-consuming. Thus, a number of generation-based automatic code review (ACR) approaches have been proposed recently, which leverage deep learning techniques to automate various activities in the code review process (e.g., code revision generation and review comment generation).We find the previous works carry three main limitations. First, the ACR approaches have been shown to be beneficial in each work, but those methods are not comprehensively compared with each other to show their superiority over their peer ACR approaches. Second, general-purpose pre-trained models such as CodeT5 are proven to be effective in a wide range of Software Engineering (SE) tasks. However, no prior work has investigated the effectiveness of these models in ACR tasks yet. Third, prior works heavily rely on the Exact Match (EM) metric which only focuses on the perfect predictions and ignores the positive progress made by incomplete answers. To fill such a research gap, we conduct a comprehensive study by comparing the effectiveness of recent ACR tools as well as the general-purpose pre-trained models. The results show that a general-purpose pre-trained model CodeT5 can outperform other models in most cases. Specifically, CodeT5 outperforms the prior state-of-the-art by 13.4%–38.9% in two code revision generation tasks. In addition, we introduce a new metric namely Edit Progress (EP) to quantify the partial progress made by ACR tools. The results show that the rankings of models for each task could be changed according to whether EM or EP is being utilized. Lastly, we derive several insightful lessons from the experimental results and reveal future research directions for generation-based code review automation.
Interpretation-based Code Summarization

Code comment, i.e., the natural language text to describe the semantic of a code snippet, is an important way for developers to comprehend the code. Recently, a number of approaches have been proposed to automatically generate the comment given a code snippet, aiming at facilitating the comprehension activities of developers. Despite that state-of-the-art approaches have already utilized advanced machine learning techniques such as the Transformer model, they often ignore critical information of the source code, leading to the inaccuracy of the generated summarization. In this paper, to boost the effectiveness of code summarization, we propose a two-stage paradigm, where in the first stage, we train an off-the-shelf model and then identify its focuses when generating the initial summarization, through a model interpretation approach, and in the second stage, we reinforce the model to generate more qualified summarization based on the source code and its focuses. Our intuition is that in such a manner the model could learn to identify what critical information in the code has been captured and what has been missed in its initial summarization, and thus revise its initial summarization accordingly, just like how a human student learns to write high-quality summarization for a natural language text. Extensive experiments on two large-scale datasets show that our approach can boost the effectiveness of five state-of-the-art code summarization approaches significantly. Specifically, for the well-known code summarizer, DeepCom, utilizing our two-stage paradigm can increase its BLEU-4 values by around 30% and 25% on the two datasets, respectively.
Implant Global and Local Hierarchy Information to Sequence based Code Representation Models

Source code representation with deep learning techniques is an important research field. There have been many studies that learn sequential or structural information for code representation. But sequence-based models and non-sequence-models both have their limitations. Researchers attempt to incorporate structural information to sequence-based models, but they only mine part of token-level hierarchical structure information. In this paper, we analyze how the complete hierarchical structure influences the tokens in code sequences and abstract this influence as a property of code tokens called hierarchical embedding. The hierarchical embedding is further divided into statement-level global hierarchy and token-level local hierarchy. Furthermore, we propose the Hierarchy Transformer (HiT), a simple but effective sequence model to incorporate the complete hierarchical embeddings of source code into a Transformer model. We demonstrate the effectiveness of hierarchical embedding on learning code structure with an experiment on variable scope detection task. Further evaluation shows that HiT outperforms SOTA baseline models and show stable training efficiency on three source code-related tasks involving classification and generation tasks across 8 different datasets.