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Traditionally university students lack motivation in subjects that are more focused on documentation and theory. This problem only deepens with each new generation. A practical workshop approach has been implemented in the subject “Analysing system requirements and specifications”. Its place in the curriculum is explained. A technique for developing innovation and prototypes, used by Google for motivation is described. The method of its’ implementation is thoroughly documented. A brief experiment in the form of a workshop is described and the gathered data is analysed. A survey on student feedback is conducted and the results are discuss ed. Influence on student soft skills improvement is evaluated. An observation on using the methodology as an introductory workshop to break the ice with engineering students is made. The conclusions made based on the feedback data and discussions with students show that the methodology is successful and student motivation and attendance is increased. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) have revolutionized various applications in natural language processing and exhibited proficiency in generating programming code. We propose a framework for evaluating the code generation ability of LLMs and introduce a new metric, pass‐ratio@n, which captures the granularity of accuracy according to the pass rate of test cases. The framework is intended to be fully automatic to handle the repetitive work involved in generating prompts, conducting inferences, and executing the generated codes. A preliminary evaluation focusing on the prompt detail, problem publication date, and difficulty level demonstrates the successful integration of our framework with the LeetCode coding platform and highlights the applicability of the pass‐ratio@n metric. [ABSTRACT FROM AUTHOR]

Machine learning is a popular tool for solving problems, however, incorporating it into a use case with additional business logic poses many challenges. Training, managing and storing many different models is not an easy task, requiring the use of multiple frameworks and languages. To take full advantage of existing frameworks it is necessary to facilitate communication between different programming languages. This paper presents an approach to integrating machine learning in a real-world use case which involves predicting demand for a diverse set of products and combining it with business rules and other components to establish a system that improves and automates the ordering process. Machine learning models are trained on real-world data from a retailer in Austria and the predictions are incorporated into a heuristic that controls and manages stock levels. This work focuses on the challenges that emerge from the integration of machine learning and presents a message bus based architecture to address them. [ABSTRACT FROM AUTHOR]

The ongoing digital transformation of industry is most clearly reflected in the increasing collection and analysis of data from various sources. Among these are sensor equipped machinery, telemetry in logistics or audiovisually monitored production floors. In order to utilize the data, e. g., to predict machinery malfunctions, its technically smooth consolidation is crucial. Therefore, numerous data interchange formats, protocols and middleware emerged over the past years. However, until today there is no gold standard technology stack for industrial data analysis, for multiple reasons, including the applications' heterogeneity. In this work, we present a software library which aims at decoupling messaging protocols and patterns from their implementation to overcome incompatibilities and, thus, facilitate data consolidation for software engineers. Moreover, we show how to use the library for rapidly modeling distributed cyber-physical systems using an integrated schema generation mechanism. Based on one real-world and one synthetic use case, we evaluate the library's applicability, discuss open issues and outline planned features. [ABSTRACT FROM AUTHOR]

Conversational agents (CA) are software programs that can converse with users using natural language. They are now widely used in various domains, such as tourism, healthcare, and others, to perform tasks and provide permanent assistance to users by interacting with them in natural language. The development of such applications is a task that requires expertise in several fields, such as software engineering, machine learning, deep learning, and natural language processing (NLP). However, several platforms and frameworks on the market facilitate the building of CA, such as Dialogflow, Rasa, and others. Recently, several research studies have proposed solutions to reduce the workload of developers and designers by offering their model-driven development approaches using domain-specific languages (DSLs), which facilitate the automation of the development of CA. This work aims to provide an Overview of CA to identify and describe their architecture and the details of its key components. and discuss the tools and technologies for their development. At the same time, discover the research topics that focus on using DSLs for model-driven development to automate and speed up the creation of these agents and discover approaches and technologies employed to implement each of these DSLs. [ABSTRACT FROM AUTHOR]

This scientific paper aims to explore the identification and quantification of the knowledge level within a team working on a software engineering project. The knowledge level of a team plays a crucial role in the success and efficiency of project outcomes. To achieve this objective, a comprehensive review of existing literature on knowledge management, team dynamics, and software engineering practices was conducted. This article reveals insights into the knowledge dynamics within software engineering teams. The research methodology consists of different quantitative and qualitative methods for data collection and analysis. The identified knowledge gaps and strengths can inform strategies for improving team performance, such as targeted training programs or knowledge sharing initiatives. Furthermore, the quantification of knowledge levels can serve as a benchmark for future projects, allowing for better resource allocation and team composition. Overall, this research is bringing benefits to the field of software engineering by providing a systematic approach to identify and quantify the knowledge level of teams, enabling organizations to optimize their team structures and enhance project outcomes. [ABSTRACT FROM AUTHOR]

To professionally plan and manage the development and evolution of the Internet of Things (IoT), researchers have proposed several IoT performance measurement solutions. IoT performance measurement solutions can be very valuable for managing the development and evolution of IoT systems, as they provide insights into performance issues, resource optimization, predictive maintenance, security, reliability, and user experience. However, there are several issues that can impact the accuracy and reliability of IoT performance measurements, including lack of standardization, complexity of IoT systems, scalability, data privacy, and security. While previous studies proposed several IoT measurement solutions in the literature, they did not evaluate any individual one to figure out their respective measurement strengths and weaknesses. This study provides a novel scheme for the evaluation of proposed IoT measurement solutions using a metrology-coverage evaluation based on evaluation theory, metrology principles, and software measurement best practices. This evaluation approach was employed for 12 IoT measure categories and 158 IoT measurement solutions identified in a Systematic Literature Review (SLR) from 2010 to 2021. The metrology coverage of these IoT measurement solutions was analyzed from four perspectives: across IoT categories, within each study, improvement over time, and implications for IoT practitioners and researchers. The criteria in this metrology-coverage evaluation allowed for the identification of strengths and weaknesses in the theoretical and empirical definitions of the proposed IoT measurement solutions. We found that the metrological coverage varies significantly across IoT measurement solution categories and did not show improvement over the 2010-2021 timeframe. Detailed findings can help practitioners understand the limitations of the proposed measurement solutions and choose those with stronger designs. These evaluation results can also be used by researchers to improve current IoT measurement solution designs and suggest new solutions with a stronger metrology base. [ABSTRACT FROM AUTHOR]

Emerging technologies in artificial intelligence (AI) and advanced optimization methodologies have opened up a new frontier in the field of software engineering. Among these methodologies, optimization algorithms such as the multi-verse optimizer (MVO) provide a compelling and structured technique for identifying software vulnerabilities, thereby enhancing software robustness and reliability. This research investigates the feasibility and efficacy of applying an augmented version of this technique, known as the test path distance multiverse optimization (TPDMVO) algorithm, for comprehensive path coverage testing, which is an indispensable aspect of software validation. The algorithm's versatility and robustness are examined through its application to a wide range of case studies with varying degrees of complexity. These case studies include rudimentary functions like maximum and middle value extraction, as well as more sophisticated data structures such as binary search trees and AVL trees. A notable innovation in this research is the introduction of a customized fitness function, carefully designed to guide TPDMVO towards traversing all possible execution paths in a program, thereby ensuring comprehensive coverage. To further enhance the comprehensiveness of the test, a metric called 'test path distance' (TPD) is utilized. This metric is designed to guide TPDMVO towards paths that have not been explored before. The findings confirm the superior performance of the TPDMVO algorithm, which achieves complete path coverage in all test scenarios. This demonstrates its robustness and adaptability in handling different program complexities. [ABSTRACT FROM AUTHOR]

With the rapid expansion of the software sector in recent decades, companies' standards for new employees become more stringent as well. Specifically, they are often unsatisfied with the insufficient competence of students in handling complex assignments in the software development process. To address these issues as well as to help students become acquainted with the actual development process in software engineering, we developed a card game that simulates concepts, roles, and tasks of the actual scenarios for software engineering education. To test the effectiveness of the game, we experimented with two groups of 42 students aiid measure the results using a post-test and a post-questionnaire. Experimental results show that our approach increased students' learning motivation and help students better understand knowledge in software engineering lessons. These potential results make a call for the use of game-based learning in software engineering education to increase students' learning engagement and outcomes. [ABSTRACT FROM AUTHOR]

Agile software development, a paradigm that emphasizes project team autonomy and the value of responding to changes over following standardized processes, has gained prominence in the software industry. Prior investigations on the adoptions of agile paradigms for software operations and their performance implications have typically focused on isolated aspects of software development processes. In this study, we adopt a configurational perspective of software operations and assess the causal impacts of adopting an organizational policy that grants higher levels of autonomy to project teams. Building on the equifinality framework proposed in organizational studies, we posit that an organizational policy that provides higher levels of autonomy for software teams engenders performance-enhancing adaptations through agile reconfigurations of project operations. To test our hypothesis, we collaborated with a commercial software firm and collected data from a policy experiment at the firm. We examined projectlevel data spanning a four-year observation period during which the firm implemented a new policy that significantly reduced the hurdles for project teams to autonomously reconfigure their operations. The results support our postulation and shed light on how an organizational policy that provides greater autonomy to software teams for designing their context-specific project configurations can improve project performance. [ABSTRACT FROM AUTHOR]

The present work is the result of applied research, which describes how the physical therapy program at the Universidad Santiago de Cali approached the support of the learning processes from the Guide to Physical Therapist Practice issued by the American Physical Therapy Association (APTA) from a teaching perspective using information technologies with an emphasis on mobile devices (D-Learning). The implementation process was conducted using the PSP (Personal Software Process) methodology, condensing its six characteristic moments to address the problem in four stages: planning, design, development, and validation, corresponding to phases 2 and 3 of the interdisciplinary project developed by and between the Schools of Engineering and Health Sciences, thereby understanding that the remaining phases exceed the scope of this paper (these phases include a systematic review, an analysis, and feedback from the academic community). A preliminary assessment describes the knowledge gathering and idea conception processes, as well as the solution design process in Enterprise Architect. Subsequently, the prototype was implemented, the corresponding documentation was prepared, and its usability was validated by the academic community at the university. Therefore, a supporting tool was generated, focusing specifically on learning about the Guide to Physical Therapist Practice. [ABSTRACT FROM AUTHOR]

Developing the communication skills of software engineering graduates to meet industry requirements is a challenge for educators. This study presents a project-based learning framework that promotes students' communication skills in a software engineering project course. The questionnaire on selfefficacy for software development (CSESD) was designed for students' self-assessment of their confidence in communication skills. Findings indicate that students' CSESD increased significantly after the course ended. Educators can apply the designed framework to software development-related project courses. The CSESD questionnaire can be used to assess students' confidence in their communication skills and assist educators in preparing students' readiness before graduation. [ABSTRACT FROM AUTHOR]

This research develops learning materials for Computer Numerical Control (CNC) programming courses based on virtual reality technology. The impact of the Covid-19 pandemic on social activities, one of which is the educational aspect, has changed the learning pattern to a massive one where the learning process that should be carried out face-to-face, but now must carry out distance learning. Therefore, alternative learning media is needed to support the practicum process from a distance by utilizing virtual reality technology that is simulated using digital devices. The Software engineering models, namely: requirements, design, implementation, verification, and maintenance phases are the five primary stages used in this study, which uses the waterfall development model research method. The feasible of the virtual laboratory obtained the results of material validation, learning media, and small group trials of 12 diploma students of Mechanical Engineering at Universitas Negeri Padang. The findings of the study found that small group trials in CNC virtual laboratory can be successfully utilized in CNC programming courses. This research is expected to have implications for improving learning outcomes in CNC programming courses by using virtual laboratory technology in creating a conducive and quality learning atmosphere, so that the learning material provided can be accepted by students. [ABSTRACT FROM AUTHOR]

Positive consideration of software engineering codes of ethics by computing educators promotes inclusion in the teaching of software development courses. For computing educators, this is significant because they contribute immensely to the development of software engineering graduates, not only in terms of teaching technical skills but also in ethical development. This study aims to investigate the perceived importance of codes of ethics by lecturers who teach software development courses at a University of Technology in South Africa. The data was collected using an online survey from 103 educators from two computing departments in a South African UoT; 44 responses were received. Data was analyzed using descriptive statistics to evaluate the responses; the Pearson Chi-square test was applied to assess the level of association between variables of interest for more conclusive results in addressing the objective of the study. The results of this study indicated that the majority of participants were males; female participants amounted only to 18.2 per cent. Results also reported that most participants agreed with all the statements tested to determine the perceived importance of Software Engineering Codes of Ethics to educators. In addition, an association was presented between the importance of a software engineering code of ethics to an educator and three other variables (the need to teach students about ethical behaviour, an obligation for software engineers to consider the ethical implications of their systems and sex of the respondents) respectively. This study recommended that institutions of higher learning consider finding permanent ways of inculcating a culture of ethical conduct into its staff members, encouraging educators to take up professional memberships with professional bodies. These measures will ensure that software development educators are trained to maintain high standards within their profession, embracing the use and adherence to a code of ethics in the teaching of software development courses. [ABSTRACT FROM AUTHOR]

A measure of the "goodness" or efficiency of the test suite is used to determine the proficiency of a test suite. The appropriateness of the test suite is determined through mutation analysis. Several Finite State Machine (FSM) mutants are produced in mutation analysis by injecting errors against hypotheses. These mutants serve as test subjects for the test suite (TS). The effectiveness of the test suite is proportional to the number of eliminated mutants. The most effective test suite is the one that removes the most significant number of mutants at the optimal time. It is difficult to determine the fault detection ratio of the system. Because it is difficult to identify the system's potential flaws precisely. In mutation testing, the Fault Detection Ratio (FDR) metric is currently used to express the adequacy of a test suite. However, there are some issues with this metric. If both test suites have the same defect detection rate, the smaller of the two tests is preferred. The test case (TC) is affected by the same issue. The smaller two test cases with identical performance are assumed to have superior performance. Another difficulty involves time. The performance of numerous vehicles claiming to have a perfect mutant capture time is problematic. Our study developed three metrics to address these issues: FDR/|TS|, FDR/|TC|, and FDR/|Time| generation tools were examined and tested using the developed metrics. Thanks to the metrics we have developed, the research contributes to eliminating the problems related to performance measurement by integrating the missing parameters into the system. [ABSTRACT FROM AUTHOR]

Documentation as Code (DaC) is an approach that applies the principles of software development to the production of technical documentation. Using modern tools, DaC enables software engineers to treat documentation as a first-class citizen in the development process, alongside code and tests. In this paper, we discuss the advantages of DaC in system and software engineering, including improved accuracy, traceability, and maintainability. In the automotive industry, DaC has been used to document various aspects of vehicle development, such as requirements, design, testing, and compliance. This paper provides an overview of the state-ofthe-art in DaC in the automotive industry and discusses the potential benefits and challenges of using this approach. In addition, case studies and examples of how DaC has been used in the automotive industry to improve the quality and maintainability of documentation are presented. This research has been conducted with more than 150 engineers actively contributing to DaC on the project for more than a year within a company, so the scalability of the presented solution has been tested. Finally, a set of guidelines is provided for teams to follow when adopting DaC to ensure successful implementation. [ABSTRACT FROM AUTHOR]

The role of data mining in software engineering is obvious, but there is a lack of mining depth. In the past, engineering layout and function distribution problems in software development, and the framework construction was unreasonable. Therefore, this paper proposes a software system based on data mining technology. First, the software engineering project is divided, and the software engineering project design is carried out according to the interactive requirements to realize the preprocessing of software engineering. Then, according to the design criteria, the design collection of software engineering is formed, and the information system is analyzed in depth. MATLAB simulation shows that the design interactivity and accuracy of data mining are superior to traditional design methods under the condition that software engineering requirements are consistent. [ABSTRACT FROM AUTHOR]

With the development of internet technology, cloud computing is becoming increasingly popular, and it has a wide range of applications in various fields, such as mobile payments and the Internet of Things. The big data model is a very important and valuable set of useful and unique information. This article mainly introduces the establishment of an object-oriented architecture-based machine learning system classification model using big data analysis methods, as well as the use of neural network algorithms to construct machine learning system classification patterns. Through examples, a comparative experiment is conducted to verify the effectiveness of traditional manual annotation modeling methods combined with parallel processing. Its experimental results show that the model has high accuracy, with an accuracy rate above 92% and a recall rate above 94%, and its F1 value is infinitely close to 1, indicating that the average accuracy and precision of the model is very high. [ABSTRACT FROM AUTHOR]

With the advent of Internet information technology, data mining technology has been applied in various fields of China's social and industrial development, and has promoted the quality development of the industry [1]. Nowadays, people are widely influenced by Internet computer technology, and the application of computer technology has been indispensable in life, work and study. At the same time, data mining technology arises from Internet communication technology and is used as an important technical means of operation and development by various industries, especially the application of data mining technology in university software engineering teaching is becoming more and more widespread, but there are still many unavoidable problems that require scholars to pay more attention. The article analyzes and discusses the significance of applying data mining technology in software engineering [2] , and on this basis, the application path of data mining technology in software engineering database design is proposed, including: mining information, mining loopholes, mining execution records and open source. It is hoped that relevant technology users can efficiently apply data mining technology to make software engineering It is more reasonable and efficient, so that software engineering can be further developed. This paper presents the implementation and performance analysis of the teacher management decision-making system based on data mining, and introduces the implementation process of the system in detail. [ABSTRACT FROM AUTHOR]

Human-Computer Interaction (HCI) is a sub-area within computer science focused on the study of the communication between people (users) and computers and the evaluation, implementation, and design of user interfaces for computer systems. HCI has accomplished effective incorporation of the human factors and software engineering of computing systems through the methods and concepts of cognitive science. Usability is an aspect of HCI dedicated to guaranteeing that human-computer communication is, amongst other things, efficient, effective, and sustaining for the user. Simultaneously, Human activity recognition (HAR) aim is to identify actions from a sequence of observations on the activities of subjects and the environmental conditions. The vision-based HAR study is the basis of several applications involving health care, HCI, and video surveillance. This article develops a Fire Hawk Optimizer with Deep Learning Enabled Activity Recognition (FHODL-AR) on HCI driven usability. In the presented FHODLAR technique, the input images are investigated for the identification of different human activities. For feature extraction, a modified SqueezeNet model is introduced by the inclusion of few bypass connections to the SqueezeNet among Fire modules. Besides, the FHO algorithm is utilized as a hyperparameter optimization algorithm, which in turn boosts the classification performance. To detect and categorize different kinds of activities, probabilistic neural network (PNN) classifier is applied. The experimental validation of the FHODL-AR technique is tested using benchmark datasets, and the outcomes reported the improvements of the FHODLAR technique over other recent approaches. [ABSTRACT FROM AUTHOR]

As a young discipline Computer Science suffers from a crisis of identity when trying to best approach research problems and conduct quality and rigorous scientific work. This is commonly handled by borrowing scientific methods from mature disciplines, such as Mathematics and Logic theories. However, whilst the object of investigation in Computer Science changes both the construction of theories describing it and the growing practical experience in its usage, accepted scientific methods do not respond well to interplaying with theoretical and practical approaches. Particularly, emergent interdisciplinary fields within Computer Science, such as Online Education, show no consensus in literature about well-defined methods to conduct systematic research, thus facing difficulties to deal with interdisciplinary research and methodological gaps. The ultimate goal of this study is to shed light on these difficulties whilst proposing methodological guidelines and good practices in order to foster sound research in the Online Education field. [ABSTRACT FROM AUTHOR]

Software deffect prediction, a problem of major relevance within the search-based software engineering field, aims to enhance software quality by early and precisely uncovering faulty software modules. Accurate detection of software defects in new software releases might increase the performance of the software development process in terms of cost, time and software quality. Most approaches from the software deffect prediction literature try to develop general solutions that are designed to work with any type of software deffect. From a software engineering perspective, software defects may take various forms and identifying/fixing different types of defects requires different approaches. Starting from the assumption that specific types of software defects have a particular behaviour, we are introducing in this paper, as a proof of concept, an unsupervised learning-based methodology for mining behavioural patterns for specific classes of software defects and identifying features which would be relevant for detecting the uncovered classes. The experiments performed on an open-source software deffect prediction data set collected from all releases of the Apache Ivy software highlight that the results obtained by applying the proposed methodology are highly correlated with the way human domain experts categorise and address software defects. Creating software deffect prediction models that are specifically tailored for different software deffect types may improve the accuracy of the developed models, open the possibility to apply different sets of predictive models based on the domain of the software and may accelerate the adoption of software deffect prediction approaches by the industry. [ABSTRACT FROM AUTHOR]

In this study, we propose a method for applying a temporal relation identification model to functional requirements. We discuss the limited availability of data in the requirements engineering domain compared to other fields when used for supervised learning, and therefore employ a corpus from the news domain for training. The experimental results demonstrate that the types of temporal relations present in functional requirements are limited, indicating that focusing on learning with a narrowed set of labels is effective. Additionally, We incorporate Dependency Path (DP) into the temporal relation identification model and report, through comparative experiments, that leveraging DP is effective, but minor modifications to DP do not lead to significant improvements in accuracy. By demonstrating specific application methods of temporal relation identification in requirements engineering, we anticipate contributing to the analysis of functional requirements in software development. [ABSTRACT FROM AUTHOR]

The presence of artificial intelligence (AI) is spreading fast through almost every industry, and health care is no exception. Data-based decision-making software is becoming pervasive in all facets of modern life, and the AI-enabled chatbot ChatGPT is having a seismic impact on the public perception of AI. [ABSTRACT FROM AUTHOR]

Blended Learning (BL) combines the advantages of both in-person and online learning while allowing students to affect their learning schedules and take responsibility. The capabilities of online education took worldwide interest during the COVID-19 pandemic, with the need to better understand online education's impact on educational achievements and how technical environments could provide learning experiences to replace face-to-face sessions at the campus. We examine the trends towards online learning on the recently published articles during the COVID-19 pandemic, comparing thorn to a learning survey conducted in the European Union in 2021. The study's objective is to identify the current trends and the effect of the COVID-19 pandemic on online education. This paper compares our findings from a systematic literature review against the trends observed from qualitative survey data collected from six European countries. Our mapping study identified several trends, such as that long-distance education had become a long-term strategy in higher education compared to the pre-COVID-19 era and that fully online education can be very exhausting for students, causing retention problems with those who need more skills for independent studies. The BL methods engage students and allow them to design their learning schedules. and after COVID-19, these methods are becoming long-term strategies for education. However, these approaches also require skills in the course design to ensure that other aspects and needs, such as social inclusiveness to motivate students, are sufficiently addressed due to the need for classroom interactions and peer support from shared campus experiences. [ABSTRACT FROM AUTHOR]

Recently, spectrum-based fault localization approaches have been widely used for its fast and perform well for programs with only one fault.However, most of the existing methods do not consider the fact that the programs tend to have multiple faults. To address the above issue, we propose a Particle Swarm Optimization with genetic operation based Multiple-Fault Localization (PSOMFL). Our method models the software multiple-fault localization process as a search process for the particle swarm algorithm, which can quickly find the optimal solution in the multi-dimensional hyper-volume, and finally analyzes the optimal solution set to obtain the locations of multiple faults. We have implemented a prototype and conducted several experiments to compare PSOMFL against the existing fault localization approaches. The experimental results show that PSOMFL outperforms the compared methods and can reduce the costs by 5%-25% on average. [ABSTRACT FROM AUTHOR]

Copyright of Avances is the property of Instituto de Informacion Cientifica y Tecnologica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Detecting and fixing vulnerabilities in software programs before production is crucial in software engineering. Manual vulnerability detection is labor-intensive, especially for large programs, leading to the proposal of machine learning-based methods for automation. However, existing approaches primarily detect vulnerabilities at the function level, providing non-specific results that require additional developer effort to locate vulnerabilities. Detection at the line-of-code level is an underexplored area. In this paper, we propose a novel deep learning method for line-of-code vulnerability detection. Our hybrid neural network combines a memory network and multi-head attention mechanism. Through comprehensive experiments, we analyze the impact of each modification, demonstrating significant improvements in performance. Our approach outperforms existing methods for comparison, showcasing its effectiveness in vulnerability detection. [ABSTRACT FROM AUTHOR]

Due to recent industry needs, synergies between data science and software engineering are starting to be present in data science and engineering academic programs. Two synergies are: applying data science to manage the quality of the software (software analytics) and applying software engineering best practices in data science projects to ensure quality attributes such as maintainability and reproducibility. The lack of these synergies on academic programs have been argued to be an educational problem. Hence, it becomes necessary to explore how to teach software analytics and software engineering best practices in data science programs. In this context, we provide hands-on for conducting laboratories applying project-based learning in order to teach software analytics and software engineering best practices to data science students. We aim at improving the software engineering skills of data science students in order to produce software of higher quality by software analytics. We focus in two skills: following a process and software engineering best practices. We apply project-based learning as main teaching methodology to reach the intended outcomes. This teaching experience shows the introduction of project-based learning in a laboratory, where students applied clara science and best software engineering practices to analyze and detect improvements in software quality. We carried out a case study in two academic semesters with 63 data science bachelor students. The students found the synergies of the project positive for their learning. In the project, they highlighted both utility of Lising a CRISP-DM data mining process and best software engineering practices like a software project structure convention applied to a data science project. [ABSTRACT FROM AUTHOR]

Enterprise Resource Planning (ERP) is a business system that supports most of the critical processes of a company. It helps maintain a unified and reliable repository of information for decision-making. Implementing an ERP is a complex project, which implies a high level of effort and investment. Although the methodologies provided by the leading ERP providers are beneficial, there is still a high failure rate in the implementation. Many authors have analyzed the factors and causes of these implementation failures. There is also research to propose new implementation models that replace the existing ones. However, there is little research on existing methodologies incorporating new concepts from other disciplines to improve them. In this research, we propose a model that complements the current methodologies and uses the best practices in project management and software engineering directly related to the issues found in the literature. We submitted it to a group of experts on the subject to validate the model based on the Delphi method. [ABSTRACT FROM AUTHOR]

Environmental monitoring systems have been evolving dynamically to embrace modern Internet of Things (IoT) technology in the last decade. Despite this progress there are, however, continuing limitations and issues with some IoT designs. Thus, past research has identified areas of concern in areas such as communications, interoperability, reliability, and scalability. As enabling technologies evolve in an accelerated manner, there will no doubt be a plethora of environmental monitoring solutions under development. Such solutions need to be well evaluated so that potential users are knowledgeable in relation to the best solution for their specific applications. Along these lines, this paper puts forward a framework to evaluate proposed IT designs relevant to smart environmental monitoring systems. This framework is based on model standardized software engineering requirements found in ISO 25010, which it uses as an aid to develop business-driven 'smart' environmental monitoring systems. [ABSTRACT FROM AUTHOR]

Topic modelling is a probabilistic based statistical model used to find the latent topics that best depicts the content of the documents. Community Question Answering websites such as Quora, Stack Overflow and Yahoo! Answers have been prevalently in use, performs topic modeling as lot of queries pour in on daily basis which make it challenging to understand, summarize and synthesize the main topic of discussions. On these websites there are basically two sources of information that are available to analyze the key latent topics: questions text and tags. Questions are in textual format and tags are the keywords or tokens that are related to the question being asked which describes the content of the question. In past studies, most of the researchers have used question text for the purpose of topic modeling. It is still unclear why tag is not being considered for topic modeling. To combat this issue, this paper performs topic modeling using both question tags and text. The topic modeling based on tags has been compared with text based on two metrics namely coherence and perplexity. Experiment has been conducted on three real time datasets namely Artificial intelligence, Software Engineering and quantum computing from Stack exchange website. At high level tag-based topic modelling looked promising but closer observation revealed the opposite. It has been found that topic modeling using question text is preferable as topic modelling using tags collapses after a certain number of topics. [ABSTRACT FROM AUTHOR]

The article carries out a quantitative analysis of online courses on learning the Python programming language as of October 2022. Such online platforms as Codecademy, Alison, FutureLearn, Udemy, Edx were considered. The main requirements for software engineers training, as well as features of online courses on learning programming languages were highlighted. The authors analyzed samples of online courses, which were a) generated with an automatic search by the keyword "Python" and b) found manually by relevant thematic sections. The analysis was carried out separately for each sample according to various criteria: a number of courses, an approximate level of student training, the cost of taking an online course, and a possibility of obtaining a certificate/diploma. The largest number of courses on learning Python is presented on Udemy platform. Alison platform offers the fewest number of courses for learning the Python language. All platforms are aimed at different level of student training (beginner, intermediate, advanced). On Alison and Edx platforms, 100% of the courses are free, while on FutureLearn platform, all courses are payable. After completing online courses, students can receive a certificate (all MOOC) and/or a diploma (Alison, Edx). As Python popularity is growing, further research is planned to analyze online courses which are represented on various MOOC and are related to the use of the Python programming language in different areas. [ABSTRACT FROM AUTHOR]

Copyright of Investigación Bibliotecológica is the property of UNAM, Centro Universitario de Investigaciones Bibliotecologicas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Iranian Journal of Information Processing & Management is the property of Iranian Information & Documentation Center (IRANDOC) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The process of assigning bugs to particular programmers is called bug assignment in software engineering. The programmer can fix the bugs by applying their knowledge. This research article presents an Ant colony optimization-based programmer recommendation model (ProRE) to manage software bugs precisely. The proposed ProRE model performs four operations: data pre-processing, i.e., data Pre-processing, extraction, feature selection, and programmer recommendation process. The feature selection stage utilized the Ant colony optimization (ACO) method to determine the appropriate subsets of features from all features. In the programmer recommendation stages, three programmer metrics, i.e., functionality ranking, bug occurrence, and mean Bug fixing time, are utilized for the recommendation assignment. The effectiveness of the proposed programmer recommendation system is assessed using datasets from Mozilla, Eclipse, Firefox, JBoss, and OpenFOAM. It is noted that the proposed model offers a better recommendation strategy over the other available systems. The simulation findings of the proposed ProRE model are also analyzed with well-known available ML methods, i.e., SVM, NB, and C4.5. It is observed that the recommendation results have improved by an average of 4%, 10%, and 12% compared to SVM, C4.5, and NB-based models. Programmer recommendation software is implemented for allocating the bugs to accurate programmers. It has been found that the proposed ProRE model generates more optimistic outcomes than existing ones. [ABSTRACT FROM AUTHOR]

One of the biggest challenges while migrating from a monolith architecture to a microservice architecture is to define a proper communication technology. In monolith applications, communication between components is performed using the in-process method or function calls, while different communication methods have to be established to achieve the same functionality in a microservice architecture. A microservices-based application is a distributed system running on multiple processes or services. Therefore, microservices must interact using interprocess communication technologies. This research aims to evaluate synchronous and asynchronous communication technologies and determine particular cases for their application while decomposing monolith into cloud-native applications. Five communication technologies, such as HTTP Rest, RabbitMQ, Kafka, gRPC, and GraphQL, have been evaluated and compared by proposed evaluation criteria. The advantages and disadvantages of each communication technology were identified in the context of microservices architecture. [ABSTRACT FROM AUTHOR]

Component-based software engineering is concerned with the development of software that can satisfy the customer prerequisites through reuse or independent development. Coupling and cohesion measurements are primarily used to analyse the better software design quality, increase the reliability and reduced system software complexity. The complexity measurement of cohesion and coupling component to analyze the relationship between the component module. In this paper, proposed the component selection framework of Hexa-oval optimization algorithm for selecting the suitable components from the repository. It measures the interface density modules of coupling and cohesion in a modular software system. This cohesion measurement has been taken into two parameters for analyzing the result of complexity, with the help of low cohesion and high cohesion. In coupling measures between the component of inside parameters and outside parameters. The final process of coupling and cohesion, the measured values were used for the average calculation of components parameter. This paper measures the complexity of direct and indirect interaction among the component as well as the proposed algorithm selecting the optimal component for the repository. The better result is observed for high cohesion and low coupling in component-based software engineering. [ABSTRACT FROM AUTHOR]

With the increase of the content and difficulty of software engineering education courses, software engineering education visual training system came into being, but the technology is not mature at present, and the representation learning algorithm part of the visual training system needs to be optimized. In order to solve this problem, the research proposes to optimize the take model by using the trans representation algorithm, and embed the optimized knowledge map into the new software engineering education visual training system. The performance of TEKEE model is verified by comparing TEKEE model with Trans E model, Trans D model and TEKED model. The experimental results show that the MR value of the optimized TEKEE model is 62, Hits@10 The value is 0.92, which is better than the other three representation learning models. In terms of the bearing capacity test of the visual training system, the response time of the business operation of the research and design training system is 1.22 seconds, and the CPU occupancy of the application server is 12.5%, all of which are normal. The experimental results show that the performance of the optimized TEKEE model has indeed been greatly improved, and the visual training system composed of the optimized knowledge map has a very good carrying capacity, which can provide a new idea for the software engineering education and training system. [ABSTRACT FROM AUTHOR]

An interesting challenge in software requirements engineering is converting textual requirements to Business Process Model and Notation (BPMN) diagrams. In this study, the BPMN diagram is used as an intermediate representation before measuring the functional software size from Natural Language (NL) input. The methods currently used for converting NL input to BPMN diagrams are not able to generate complete BPMN diagrams, nor can they handle complex and compound-complex sentences in the NL input. This study proposes conversion from textual requirements to a BPMN diagram for improving the weaknesses of existing methods. The proposed method has two stages: 1) analyzing the textual requirements using natural language processing and 2) generating the BPMN diagram. The output of the first stage is fact types as the basis for generating the BPMN diagram in the second phase. The BPMN diagram is generated using a set of informal mapping rules that were created in this study. The proposed method was applied to ten textual requirements of an enterprise application, which involved simple, compound, complex, and compound-complex sentences. The experiments resulted in a suitable BPMN diagram with higher accuracy than obtained by other methods. [ABSTRACT FROM AUTHOR]

In complex information systems, some features may recur hundreds of times. Therefore, identifying such features and suggesting suitable design solutions for them can simplify the development and maintenance of such complex systems. In that regard, this work introduces five design patterns that were utilized to develop data management features that recurred many times in several web-based information systems used to manage enterprise and student data at the German Jordanian University. In this context, a software design pattern describes a solution to design repeating software features. The proposed design patterns are documented in a general manner using UML diagrams to enable utilizing them in different web development platforms and to allow their development using popular object-oriented programming languages. In particular, the suggested patterns seek to solve the following software features: flexible user interface for data management, reusable module for dependent dropdown filters, table data lazy loading, unified modules to handle data addition and editing, and page state restoration when navigating between related pages. Not to mention, the validation results show that the discussed design patterns were used hundreds of times while implementing six information systems for the university. Specifically, one of the patterns was utilized more than 700 times. Additionally, as it seems that some of the design patterns in this work were not investigated in related work. [ABSTRACT FROM AUTHOR]

Software defect prediction is a major concern for estimating many factors of software products such as reliability maintenance, estimating the cost, and quality assurance. Under different circumstances/phases, the defects can be expected before scheduling each stage of software development. However, most of the software products are being developed by individuals, which leads to unwanted types of defects in different scenarios. Software structural quality refers to analyzing the source code, its inner structure, and compliance with the functional requirements. In this work, an Adaptive Boosting Meta-estimator has been proposed for software defect prediction using characterize code features associated with software quality. The proposed method has been tested with various performance metrics and compared with existing machine learning-based methods to prove its superiority. [ABSTRACT FROM AUTHOR]

An interesting challenge in software requirements engineering is converting textual requirements to Business Process Model and Notation (BPMN) diagrams. In this study, the BPMN diagram is used as an intermediate representation before measuring the functional software size from Natural Language (NL) input. The methods currently used for converting NL input to BPMN diagrams are not able to generate complete BPMN diagrams, nor can they handle complex and compound-complex sentences in the NL input. This study proposes conversion from textual requirements to a BPMN diagram for improving the weaknesses of existing methods. The proposed method has two stages: 1) analyzing the textual requirements using natural language processing and 2) generating the BPMN diagram. The output of the first stage is fact types as the basis for generating the BPMN diagram in the second phase. The BPMN diagram is generated using a set of informal mapping rules that were created in this study. The proposed method was applied to ten textual requirements of an enterprise application, which involved simple, compound, complex, and compound-complex sentences. The experiments resulted in a suitable BPMN diagram with higher accuracy than obtained by other methods. [ABSTRACT FROM AUTHOR]