Reviewerly/exHarmony huggingface dataset:
On the Prediction of Software Merge Conflicts: A Systematic Review and Meta-analysis

Context: Predicting software merge conflicts plays a chief role in many software engineering tasks. Prediction techniques can be used to identify and anticipate potential conflicts between source code snippets that may arise when merging changes made by multiple developers. Problem: When multiple developers are working on the same source code, they may make changes to the same code snippets, which can cause conflicts when the changes are brought together. The problem is that typically these conflicts are difficult and time-consuming to resolve, and can potentially cause serious problems if not addressed properly. Solution: Predicting when conflicts will help in prioritizing or better managing the changes to be made in the code, in order to mitigate the emergence of conflicts. In this sense, developers can use tools that exam source codes to pinpoint potential snippets where conflicts are likely to occur. Theory of IS: This work was conceived under the aegis of the General Theory of Systems, in particular with regard to the interfaces between the parts of a system within its borders. In this case, the parts are themselves independent systems, called constituents, which include some information systems (IS). Method: This article focus on providing a systematic review and meta-analysis of published studies on the prediction of software conflicts. For this, we follow the PRISMA method. Contributions and Impact in the IS area: Development of a database knowledge of the historical context over predicting conflicts that will help in the development of future work and other research.
Evaluating Students’ Technology Acceptance of Use of Metaverse as an Educational Information System for Hybrid Education

Context: COVID-19 has brought new challenges and chances to modern society. During that time, Information Systems (IS) become more indispensable to people’s lives. Especially in the educational field, new technologies and ISs, such as metaverses, arose as innovations for remote and blended education. Problem: However, there needs to be more research on using metaverses as educational ISs applied to the hybrid context. Solution: In this study, we explored a metaverse as an educational IS in a real context for an undergraduate IS course. Method: As a research method, we used a quasi-experimental, aiming to evaluate students’ perceptions and acceptance of using the metaverse as IS to support blended education. IS Theory: All the evaluation was designed based on the Technology Acceptance Model (TAM). Summary of Results: We analyzed data through a quantitative approach, using descriptive and inferential statistics. As a result, we observed evidence of the acceptance of metaverse as educational IS in the blended context. It was considered easy and useful by students. Contributions to IS: Therefore, we proposed new technologies being applied as IS for educational contexts. With our research, we have been covering a need for studies about metaverses for blended purposes. Thus, we glimpse reflections and opportunities to use those metaverse systems in other themes in which the blended context could be advantageous.
Characterizing Toolkits for Platform Independent Chatbot Development

Context: With the increase in the use of conversational agents, especially those based on written language (chatbots), users can interact with machines through natural language. Problem: The growing demand for chatbots has raised problems in building and deploying these conversational agents to different platforms, implying adaptation costs. Solution: We performed a systematic grey literature review to identify a set of DSL-supported tools for platform-independent chatbot development. IS Theory: Not applicable. Method: This research sought to list tools and DSLs for developing platform-independent chatbots, carried out through a review of the grey literature, addressing a qualitative analysis of primary studies. Summary of Results: After conducting the studies, we discovered 14 tools and 10 DSLs supporting the construction of platform-independent chatbots. Contributions and Impact in the IS area: A characterization of tools and DSLs in state of the art supporting the construction of platform-independent chatbots.
Mock objects: a case study in industry✱

Context: Mock objects are commonly used in unit tests to isolate a class from its dependencies by substituting a dependency class instead of the original. Problem: However, in addition to the use of mock objects in the vast majority of OO applications, there are some discrepancies in their use. Solution: This work aims to present a case study of an industry application regarding the use of mock objects. The application in question is part of a private flight management system. IS Theory: We conceived this work based on the General Systems Theory, specifically to evaluate a microservice-based system’s use of testing doubles during testing automation. Method: In the analyzed application, mock object implementations are highly related to the system design. For example, the number of dependencies in a production class is directly related to the number of mocks in the respective test class. As a consequence, poor design choices are harmful to intrinsic quality factors such as the maintainability of the application tests. Our study uses metrics to analyze this practice. The research will consider factors of this system, aiming mainly to contribute to the improvement of test doubles by the test team. Summary of Results: Application of simulated objects in a similar system indicates that developers are making immature use of the technique, possibly due to system design problems. This study concludes that theory and practice are misaligned. Contributions and Impact in the IS area: The main contribution is to indicate points to improving software testing practice by using mocks correctly.
Robot Control with Imitation of Animal Behavior

The algorithms for manipulator motion control are considered. Behavior prediction elements are presented in several modes according to the environment.
Possibilities of Explainable Artificial Intelligence for Glaucoma Detection Using the LIME Method as an Example

Our study explored the experience of applying explainable artificial intelligence methods to the analysis of medical (retinal) images for glaucoma diagnosis. The possibility of applying Local Interpretable Model-Agnostic Explanations (LIME) to explain the results of artificial neural networks in the task of binary classification of retinal images for the presence of glaucoma is studied. A scale-up of the technology and its application to real clinical practice is proposed.
Perceptions of Practitioners on Security-Related Software Testing in a Mobile Software Development Company

Context: The concern with the security of software assets increases and makes the companies seek guarantees that the data stored by them is safe from unauthorized access and theft. These concerns are also applicable to the mobile software context and, as the devices have various capabilities, many security breaches may occur and expose users’ data. Thus, to guarantee security, the software testing process also includes security-related tests. Objective: empirically analyze the perceptions of practitioners from the mobile software testing environment on security-related testing topics. Method: A survey was performed among 49 software testing practitioners from a mobile software development company in Brazil regarding their perception of security testing practices. Results: We observed that there is a concern about security among the practitioners. On the other hand, the respondents indicated that there is also a lack of knowledge about the topics discussed. Conclusions: the results showed the general importance of the security testing practices by the practitioners as well as triggered the need for the creation of methods and techniques for better integration of security testing practices in the mobile software development, and also reinforced the need for improving the security culture in organizations.
Comparison of Explainable Machine-Learning Models for Decision-Making in Health Intensive Care Using SHapley Additive exPlanations

Context: Intensive Care Units (ICUs) treat patients in serious condition, demanding qualified professional assistance, modern equipment for full-time monitoring of patients, information systems for data collection, medications and other supplies. Problem: Patients can recover or die, and sepsis is one of the main causes of death. Predicting the likelihood of death in sepsis patients can help coordinate medical efforts, as incorrect initial decisions can increase the mortality rate. However, it is important that prediction models with machine learning are explainable to medical staff, so decision-making may be made conscientiously. Solution: This study aimed to identify which Machine Learning algorithms are best for predicting death by sepsis using SHapley Additive exPlanations (SHAP) to provide explainable models. Theoretical Approach: The paper draws from information processing theories based on Machine Learning and explainable artificial intelligence models. Method: 196 observations of real data were used to create Machine Learning models. Data characteristics were analyzed, followed by missing data imputation, preprocessing, feature selection and training of predictive models for SVM, Random Forest, Logistic Regression, KNN and Decision Tree. Two metrics were used to validate the models: accuracy and f1-weighted. For each generated method, SHAP values and models to generate an explainable model listing the factors that most contributed to death predictions. Summary of results: The study showed algorithms with best algorithms were SVM and Logistic Regression (80% for both metrics). The results also showed what models converged in their interpretation using the SHAP values. Contributions and Impact on the IS area: The analysis of the models generated with applied to different machine learning algorithms allow for explainable and transparent analyses by health specialists in decision-making contexts.
System-dynamic Modeling of the Decarbonization Strategy

The paper is concerned with developing a methodology to analyze complex-structured systems of control indicators within decarbonization projects. The proposed methodology is based on a formal description of control scenarios using system dynamics models, combined with methods of representation and processing of expert knowledge of causal relationships.
Identifying and Measuring Technical Debt in Software Requirements: a supporting guide

Context: Identification and measurement are the first steps in managing a Technical Debt (TD). They are essential to know the type of TD and estimate its impact on the software. Problem: However, in requirements engineering, these steps are little explored in academic research since the measurement is considered one of the most challenging phases. Solution: This work aims to develop a support guide to help software development professionals identify and measure the TD of requirements in their projects. IS Theory: This work was conceived under the aegis of the Customer Focus Theory. The requirements of TD management directly impact the quality of the product that will be delivered to the customer. Method: Initially, a systematic literature review was conducted, and a survey was applied with software development professionals allocated to different organizations. Additionally, the guide was developed and evaluated by four specialists through a focus group session. Summary of Results: Among the results, it became possible to present the existing strategies that help identify and measure the DT of requirements, in addition to tools and metrics that are used to automate the management process. Contributions and Impact in the IS area: After analyzing the results, it can be concluded that the guide is a resource that helps, especially professionals with a low level of knowledge in the area, obtain more information about TD. In addition, one of the contributions is investigating an interdisciplinary area, as TD involves social, technological, and organizational aspects.
Open Source Solutions for Software Development Platform

A comparative analysis of various approaches to changing the software tech stack in the face of significant disturbances in the market for development products has been carried out. The task of choosing a tech stack in a non-linear software development environment is formalized. On the example of the Jmix platform, the need to generate Open Source development tools is shown. The advantages of LessCode solutions are shown.
Machine-based Stereotypes: How Machine Learning Algorithms Evaluate Ethnicity from Face Data

Context: Soft biometrics is a field that aids traditional biometrics through attribute identification using descriptors such as hair type, ethnicity, or gender. When employed to large search sets, allows optimization of the search space, thus increasing performance. Problem: Ethnicity classification is controversial due to unclear or stereotypical definitions used for classifying individuals, producing incorrect results. While there are many applications that use face images to ascertain ethnicity, no extensive study has been done to map these approaches. Solution: A literature survey of the strategies, data, and results used for ethnicity classification is employed to understand how the issue is being treated in the literature. IS Theory: Considering the societal nature of ethnicity, applications that evaluate ethnicity are subjected to Social Shaping Of The Technology. Investigating these applications can help identify the societal influence of those technologies. Methods: We present a systematic literature review (SLR) to map the algorithms, types, sources of the data, and results obtained for classifying ethnicity based on the human face. Summary of Results: Many algorithms are currently used to ascertain ethnicity from face images. These algorithms may use information from the whole face or opt to use partial regions of the face. The strategies may use symbolic data about the face, like face measurements, or use subsymbolic data. The classification performance appears to be stagnated. However, datasets overrepresent White, Black, and Asian subjects, with few datasets being balanced. Contributions and Impacts in the IS area: This study offers an up-to-date look at how ethnicity classification is done, the data used, and the way society may be shaping technology.
An Empirical Evaluation of a Model for dealing with Epistemic Uncertainty in Agile Software Project Management

Context: The current trend of employing agility in software development indicates the need to manage uncertainty through its cycles of inspection and adaptation to changes.
An Approach Based on Machine Learning for Predicting Software Design Problems

Context: Software design problems emerge when internal structures of source code challenge design principles or rules. The prediction of design problems plays an essential role in the software development industry, identifying defective architectural modules in advance. Problem: The current literature lacks approaches that help software developers in predicting software design problems. Consequently, design problems end up being identified late. Solution: This article proposes a machine learning-based approach to assist software developers in predicting design problems. Theory of IS: This work was conceived under the aegis of the General Theory of Systems, in particular with regard to the interfaces between the parts of a system within its borders. In this case, the parts are themselves independent systems, called constituents, which include some information systems. Method: The research has a prescriptive character, and its evaluation was carried out through experiments and proof of concept. The analysis of the results was performed with a quantitative approach. Summary of Results: The conceived approach demonstrated to be successful, being able to identify the most relevant features and identify design problems from metrics, since classification and prediction were effective in 96% and 60% of cases, respectively. Contributions and Impact in the IS area: The main contribution is to propose an approach to classify and predict ever-present design problems in IS. Thus, our research sheds light on the need for SI maintenance to avoid architectural degradation that requires either significant maintenance effort or the complete SI redesign.
Finding Traces of Music Education on Facial Images

The paper discusses the possibility to apply machine learning to pathognomy. A hypothesis is tested if presence of classical music education could be determined by analyzing facial images. Special dataset is collected to test this hypothesis. The dataset contains facial images that are split into two classes for the binary classification problem: people with a classical music education are represented by symphony orchestra musicians while the negative class is represented by non-musicians. The quality of the dataset is explored by applying dimensionality reduction algorithms and evaluating image similarity based on feature vectors produced from original images. Two classification models are trained on different versions of the dataset. Classification is performed by a support vector machine trained on FaceNet embeddings of facial images. Both models achieve similar quality metrics on test samples. Our findings show that to certain extent it is possible to verify a person as a symphony orchestra musician based only on his or her facial image.
PharmaBulaBot: A Chatbot to Answer Drug Questions Based on Information from Pharmaceutical Package Inserts

Context: The medicine inserts or pharmaceutical inserts are documents accompanying the medicines in their packaging with information and guidance to the patient.
An Intelligent Report Generator for Chemical Documents

Context: Scientific articles and patents contain academic, industrial, and scientific information. Automatically retrieving information from these documents is necessary for supporting upcoming scientific research development.
Towards a Framework for Self-adaptive Systems Conceptual Modeling

Context: Self-adaptive Systems (SaSs) requirements engineering is challenging because it is necessary to deal with invariant requirements, which specify the system’s crisp behavior, and adaptive requirements, which specify the system’s fuzzy behavior. Problem: Our motivational question is: how to make the process of adaptive requirements conceptual modeling less complex, ensuring the coverage of the main abstractions and the comprehensibility of the resultant model? Solution: To address this, we propose a modeling framework that defines a strategy for adaptive requirements conceptual modeling based on a set of design patterns. IS Theory: This work is proposed based on the adaptive structuration theory. Method: The proposed framework was made by analyzing the main concepts related to SaSs, finding a suitable set of design patterns, specifying a modeling schema with UML, and evaluating it in focus group sessions. Summary of Results: The focus group results point out that the proposed framework supports the adaptive requirements conceptual modeling, guiding the modeling process, mainly to non-expert developers. Contributions and Impact in the IS area: Moreover, we highlight some relevant contributions of the framework, it is able to provide support for non-expert developers by guiding the SaSs conceptual modeling process. It also has an abstract modeling schema that can be applied to other concrete scenarios. Furthermore, its well-formed structures make it more comprehensible and didactic to stakeholders.
Preparation of Patient Mammogram Images for Segmentation

Early identification of breast cancer improves patient survival rates and lowers mortality and morbidity index worldwide. Several imaging techniques have been used to detect breast cancer however, mammogram images have proven to be used to detect its earliest stages. Image segmentation is crucial to image analysis as it helps to classify breast tissue into different segments making each segment more accessible for further analysis. This work aims to prepare the Chinese Mammography Database mammogram images for image segmentation.
Assis: Online Semi-Automatic Dialog Annotation Tool

Context: Task-oriented conversational systems demand a high volume of data to understand human language. One of the major challenges of Natural Language Processing (NLP) is the lack of structured annotated data to improve and refine language models, therefore, institutions often generate or mine their own data and have to annotate it themselves.
On the Issue of Individualization of Educational Trajectories in the Management of Knowledge Control (Based on Big Data)

The fundamental difference in the nature of the digital and material spheres determines the difference in the quantitative value of the criterion that makes it possible to attribute (or not to attribute) information sets characteristic of each of them to the Big Data category. In social systems, the role of a person in the construction of algorithms for preventive and corrective actions is a key one. Computational algorithms in the material sphere integrate, present in a more visual form arrays of information, reducing their dimension and making it possible to further process the formation of certain management decisions. These results acquire the greatest importance in the course of solving the problems of personalization of educational work and the development of individual educational trajectories.
A Real-time Object Detection System for the Visually Impaired

Object detection is a field in Computer Vision that is used to find the appearance of objects in images or videos by drawing a bounding box around the object. To generate very useful results to detect objects accurately, object detection frequently uses Deep Learning or machine learning. Many people usually work with deep learning rather than machine learning, because deep learning has the ability to learn high-level features than machine learning. We, humans, can quickly identify and pinpoint objects of interest by viewing images or videos. But it is a difficult task for the computer to identify objects in images or videos which requires a lot of computation to do. The main goal of this object detection is to simulate the intelligence of detecting object susing computers. In this work, we are using the You Look Only Once Version 4 (YOLO V4) algorithm and pyttsx3, for detecting the label of the object in the image or video and converting the label to speech to help the visually impaired people who have to right to live independently. In this project, objects are detected continuously from the device camera which captures multiple frames and then the audio for the detected objects will be generated. The acquired results enable the proposed prototype to be successful in enabling visually impaired users to comprehend unfamiliar environments, via an in-depth object recognition and detection model.