Breno Alves de Andrade, Rodrigo Siqueira, Lidiane Gomes
et al.
Context: Corporate training plays a strategic role in the continuous development of professionals in the software engineering industry. However, there is a lack of systematized understanding of how training initiatives are designed, implemented, and evaluated within this domain. Objective: This study aims to map the current state of research on corporate training in software engineering in industry settings, using Eduardo Salas' training framework as an analytical lens. Method: A systematic mapping study was conducted involving the selection and analysis of 26 primary studies published in the field. Each study was categorized according to Salas' four key areas: Training Needs Analysis, Antecedent Training Conditions, Training Methods and Instructional Strategies, and Post-Training Conditions. Results: The findings show a predominance of studies focusing on Training Methods and Instructional Strategies. Significant gaps were identified in other areas, particularly regarding Job/Task Analysis and Simulation-based Training and Games. Most studies were experience reports, lacking methodological rigor and longitudinal assessment. Conclusions: The study offers a structured overview of how corporate training is approached in software engineering, revealing underexplored areas and proposing directions for future research. It contributes to both academic and practical communities by highlighting challenges, methodological trends, and opportunities for designing more effective training programs in industry.
How to best use Large Language Models (LLMs) for software engineering is covered in many publications in recent years. However, most of this work focuses on widely-used general purpose programming languages. The utility of LLMs for software within the industrial process automation domain, with highly-specialized languages that are typically only used in proprietary contexts, is still underexplored. Within this paper, we study enterprises can achieve on their own without investing large amounts of effort into the training of models specific to the domain-specific languages that are used. We show that few-shot prompting approaches are sufficient to solve simple problems in a language that is otherwise not well-supported by an LLM and that is possible on-premise, thereby ensuring the protection of sensitive company data.
Masikini Lugoma, Abel Omphemetse Zimbili, Masengo Ilunga
et al.
This study uses Random Forest algorithm to model students' final year mark in an engineering technology module taught by the University of South Africa. The algorithm uses a supervised learning classification technique to map the different assessment marks and the final mark. Hence, the latter are labelled instances whereas the former constitute the features. Random Forest (RF) has been applied to Structural Analysis 3, which takes into consideration the graduate attribute concept or level of competence as far as assessments are concerned. Firstly, the RF is subjected to imbalanced binary classes, then balanced classes are achieved by Synthetic Minority Oversampling Technique (SMOTE) and class weights adjustment techniques. The results showed that SMOTE brought an improvement in accuracy of 3%. It was also revealed that an increase of 4, 15 and 9% in precision, recall and F1-Score were observed in predicting non-competent students. An increase of 4 and 3% was noticed in the case of the precision and F1-Score respectively in predicting competent students, whereas the recall did not display any change. Despite the RF with SMOTE overperformed standard RF and RF class weights adjustment, all three algorithms were good candidates in the prediction of student performance. RF-SMOTE could be suggested as a guiding instrument when dealing with imbalanced data.
This study aims to replace the manual registration system still used by PT Amanah Wisata Group with a web-based digital system. Previously, 80% of the registration process was conducted manually, often leading to errors and delays, including having to wait up to two working days to receive registration forms. The research methodology includes the collection of primary data through interviews with administrative staff and direct observation of the registration process, as well as secondary data from literature reviews and relevant documentation. The system development follows the Waterfall method, consisting of requirement analysis and system design using Unified Modeling Language (UML). The registration system also integrates a Customer Relationship Management (CRM) approach to foster stronger relationships with prospective pilgrims, improve two-way communication, and enable faster and more accurate delivery of Umrah package information and notifications. The results show that the developed system consists of three main modules: an online registration module, a pilgrim data management module, and a payment module. This system enhances administrative efficiency, reduces data entry errors, improves staff performance, and strengthens customer interaction. In conclusion, the web-based information system combined with a CRM approach not only supports the digital transformation of Umrah services but also enhances service quality and the overall experience of prospective pilgrims.
Antonio Pedro Santos Alves, Marcos Kalinowski, Daniel Mendez
et al.
[Context] In Brazil, 41% of companies use machine learning (ML) to some extent. However, several challenges have been reported when engineering ML-enabled systems, including unrealistic customer expectations and vagueness in ML problem specifications. Literature suggests that Requirements Engineering (RE) practices and tools may help to alleviate these issues, yet there is insufficient understanding of RE's practical application and its perception among practitioners. [Goal] This study aims to investigate the application of RE in developing ML-enabled systems in Brazil, creating an overview of current practices, perceptions, and problems in the Brazilian industry. [Method] To this end, we extracted and analyzed data from an international survey focused on ML-enabled systems, concentrating specifically on responses from practitioners based in Brazil. We analyzed RE-related answers gathered from 72 practitioners involved in data-driven projects. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative studies on the reported problems involving open and axial coding procedures. [Results] Our findings highlight distinct RE implementation aspects in Brazil's ML projects. For instance, (i) RE-related tasks are predominantly conducted by data scientists; (ii) the most common techniques for eliciting requirements are interviews and workshop meetings; (iii) there is a prevalence of interactive notebooks in requirements documentation; (iv) practitioners report problems that include a poor understanding of the problem to solve and the business domain, low customer engagement, and difficulties managing stakeholders expectations. [Conclusion] These results provide an understanding of RE-related practices in the Brazilian ML industry, helping to guide research toward improving the maturity of RE for ML-enabled systems.
This paper discusses the importance of reflective and socially conscious education in engineering schools, particularly within the EE/CS sector. While most engineering disciplines have historically aligned themselves with the demands of the technology industry, the lack of critical examination of industry practices and their impact on justice, equality, and sustainability is self-evident. Today, the for-profit engineering/technology companies, some of which are among the largest in the world, also shape the narrative of engineering education and research in universities. As engineering graduates form the largest cohorts within STEM disciplines in Western countries, they become future professionals who will work, lead, or even establish companies in this industry. Unfortunately, the curriculum within engineering education often lacks a deep understanding of social realities, an essential component of a comprehensive university education. Here we establish this unusual connection with the industry that has driven engineering higher education for several decades and its obvious negative impacts to society. We analyse this nexus and highlight the need for engineering schools to hold a more critical viewpoint. Given the wealth and power of modern technology companies, particularly in the ICT domain, questioning their techno-solutionism narrative is essential within the institutes of higher education.
Diana Popescu, Mariana Cristiana Iacob, Cristian Tarbă
et al.
This article proposes the integration of two novel aspects into the production of 3D-printed customized wrist-hand orthoses. One aspect involves the material, particularly Colorfabb varioShore thermoplastic polyurethane (TPU) filament with an active foaming agent, which allows adjusting the 3D-printed orthoses’ mechanical properties via process parameters such as printing temperature. Consequently, within the same printing process, by using a single extrusion nozzle, orthoses with varying stiffness levels can be produced, aiming at both immobilization rigidity and skin-comfortable softness. This capability is harnessed by 3D-printing the orthosis in a flat shape via material extrusion-based additive manufacturing, which represents the other novel aspect. Subsequently, the orthosis conforms to the user’s upper limb shape after secure attachment, or by thermoforming in the case of a bi-material solution. A dedicated design web app, which relies on key patient hand measurement input, is also proposed, differing from the 3D scanning and modeling approach that requires engineering expertise and 3D scan data processing. The evaluation of varioShore TPU orthoses with diverse designs was conducted considering printing time, cost, maximum flexion angle, comfort, and perceived wrist stability as criteria. As some of the produced TPU orthoses lacked the necessary stiffness around the wrist or did not properly fit the palm shape, bi-material orthoses including polylactic acid (PLA) inserts of varying sizes were 3D-printed and assessed, showing an improved stiffness around the wrist and a better hand shape conformity. The findings demonstrated the potential of this innovative approach in creating bi-material upper limb orthoses, capitalizing on various characteristics such as varioShore properties, PLA thermoforming capabilities, and the design flexibility provided by additive manufacturing technology.
Supaporn Sankul, Naratip Supattananon, Raknoi Akararungruangkul
et al.
This research paper introduces an adaptive differential evolution algorithm (ADE algorithm) designed to address the multi-compartment vehicle routing problem (MCVRP) for cold chain transportation of a case study of twentyeight customers in northeastern Thailand. The ADE algorithm aims to minimize the total cost, which includes both the expenses for traveling and using the vehicles. In general, this algorithm consists of four steps: (1) The first step is to generate the initial solution. (2) The second step is the mutation process. (3) The third step is the recombination process, and the final step is the selection process. To improve the original DE algorithm, the proposed algorithm increases the number of mutation equations from one to four. Comparing the outcomes of the proposed ADE algorithm with those of LINGO software and the original DE based on the numerical examples In the case of small-sized problems, both the proposed ADE algorithm and other methods produce identical results that align with the global optimal solution. Conversely, for larger-sized problems, it is demonstrated that the proposed ADE algorithm effectively solves the MCVRP in this case. The proposed ADE algorithm is more efficient than Lingo software and the original DE, respectively, in terms of total cost. The proposed ADE algorithm, adapted from the original, proves advantageous for solving MCVRPs with large datasets due to its simplicity and effectiveness. This research contributes to advancing cold chain logistics with a practical solution for optimizing routing in multi-compartment vehicles.
Industrial engineering. Management engineering, Management information systems
Tourism is one of the business fields affected by the Covid-19 pandemic. The decline in the number of tourists, both domestic and foreign, has resulted in the contribution of the tourism business sector to Indonesia's GDP decreasing. The government is now preparing plans to restore and improve tourism in tourist destination areas, one of which is DKI Jakarta in order to increase visits by domestic and foreign tourists. In achieving these goals, this study propose to utilize reviews about tourist attractions in DKI Jakarta from Google Maps and extract public opinion by conducting aspect-based sentiment analysis. Multi-label classification is a common method that is often used in aspect-based sentiment analysis. However, the multi-label approach has limited flexibility in the aspects used. One alternative method that can be used is an adaptive aspect classification method which is more flexible if there are additional new aspects used. This research aims to automate sentiment classification of tourist reviews for each aspect by developing an aspect level sentiment analysis model with an adaptive aspect classification method which will be compared with multi-label classification as a baseline method. The models used in both methods are transfer learning IndoBERT. The adaptive aspect classification method with aspect level sentiment analysis has better performance in comparison to baseline method multi-label classification with accuracy values and F1-score respectively 0.90394 and 0.71504.
Surface-enhanced Raman spectroscopy (SERS) microfluidic system, which enables rapid detection of chemical and biological analytes, offers an effective platform to monitor various food contaminants and disease diagnoses. The efficacy of SERS microfluidic systems is greatly dependent on the sensitivity and reusability of SERS detection substrates to ensure repeated use for prolonged periods. This study proposed a novel process of femtosecond laser nanoparticle array (NPA) implantation to achieve homogeneous forward transfer of gold NPA on a flexible polymer film and accurately integrated it within microfluidic chips for SERS detection. The implanted Au-NPA strips show a remarkable electromagnetic field enhancement with the factor of 9 × 10 ^8 during SERS detection of malachite green (MG) solution, achieving a detection limit lower than 10 ppt, far better than most laser-prepared SERS substrates. Furthermore, Au-NPA strips show excellent reusability after several physical and chemical cleaning, because of the robust embedment of laser-implanted NPA in flexible substrates. To demonstrate the performance of Au-NPA, a SERS microfluidic system is built to monitor the online oxidation reaction between MG/NaClO reactants, which helps infer the reaction path. The proposed method of nanoparticle implantation is more effective than the direct laser structuring technique. It provides better performance for SERS detection, robustness of detection, and substrate flexibility and has a wider range of applications for microfluidic systems without any negative impact.
Materials of engineering and construction. Mechanics of materials, Industrial engineering. Management engineering
Jacques Matthee, Kenneth Uren, George van Schoor
et al.
Simultaneous Localization and Mapping (SLAM) is a crucial component to the push towards full autonomy of robotic systems, yet it is computationally expensive and can rarely achieve real-time execution speeds on embedded platforms. Therefore, a need exists to evaluate the performance of SLAM algorithms in practical embedded environments – this paper addresses this need by creating prediction models to estimate the performance that ORB-SLAM3 can achieve on embedded platforms. The paper uses three embedded platforms: Nvidia Jetson TX2, Raspberry Pi 3B+ and the Raspberry Pi 4B, to generate a dataset that is used in training and testing performance prediction models. The process of profiling ORB-SLAM3 aids in the selection of inputs to the prediction model as well as benchmarking the embedded platforms’ performances by using PassMark. The EuRoC micro aerial vehicle (MAV) dataset is used to generate the average tracking time that the embedded platforms can achieve when executing ORB-SLAM3, which is the target of the prediction model. The best-performing model has the following results 2.84%, 3.93%, and 0.95 for MAE, RMSE and R2 score respectively. The results show the feasibility of predicting the performance that SLAM applications can achieve on embedded platforms.
Management information systems, Electronic computers. Computer science
Jung Hee Cheon, Hyeongmin Choe, Julien Devevey
et al.
We present HAETAE (Hyperball bimodAl modulE rejecTion signAture schemE), a new lattice-based signature scheme. Like the NIST-selected Dilithium signature scheme, HAETAE is based on the Fiat-Shamir with Aborts paradigm, but our design choices target an improved complexity/compactness compromise that is highly relevant for many space-limited application scenarios. We primarily focus on reducing signature and verification key sizes so that signatures fit into one TCP or UDP datagram while preserving a high level of security against a variety of attacks. As a result, our scheme has signature and verification key sizes up to 39% and 25% smaller, respectively, compared than Dilithium. We provide a portable, constanttime reference implementation together with an optimized implementation using AVX2 instructions and an implementation with reduced stack size for the Cortex-M4. Moreover, we describe how to efficiently protect HAETAE against implementation attacks such as side-channel analysis, making it an attractive candidate for use in IoT and other embedded systems.
Computer engineering. Computer hardware, Information technology
Danilo Monteiro Ribeiro, Rayfran Rocha Lima, César França
et al.
CONTEXT: Self-efficacy is a concept researched in various areas of knowledge that impacts various factors such as performance, satisfaction, and motivation. In Software Engineering, it has mainly been studied in the academic context, presenting results similar to other areas of knowledge. However, it is also important to understand its impact in the industrial context. OBJECTIVE: Therefore, this study aims to understand the impact on the software development context with a focus on understanding the behavioral signs of self-efficacy in software engineers and how self-efficacy can impact the work-day of software engineers. METHOD: A qualitative research was conducted using semi-structured questionnaires with 31 interviewees from a software development company located in Brazil. The interviewees participated in a Bootcamp and were later assigned to software development teams. Thematic analysis was used to analyze the data. RESULTS: In the perception of the interviewees, 21 signs were found that are related to people with high and low self-efficacy. These signs were divided into two dimensions: social and cognitive. Also, 18 situations were found that can lead to an increase or decrease of self-efficacy of software engineers. Finally, 12 factors were mentioned that can impact software development teams. CONCLUSION: This work evidences a set of behavioral signs that can help team leaders to better perceive the self-efficacy of their members. It also presents a set of situations that both leaders and individuals can use to improve their self-efficacy in the development context, and finally, factors that can be impacted by self-efficacy in the software development context are also presented. Finally, this work emphasizes the importance of understanding self-efficacy in the industrial context.
Chun-Ho Wu, Stephen Chi-Hung Ng, Keith Chun-Man Kwok
et al.
The proliferation of Industry 4.0 (I4.0) technologies has created a new manufacturing landscape for manufacturing, requiring that companies follow I4.0 trends to stay competitive. However, in this novel digital automated environment, these companies must also ensure that lean manufacturing principles are upheld. This study proposes a data-driven framework for analysing raw data across machines in manufacturing systems that can provide a comprehensive understanding of idle time and facilitate adjustments to reduce defect rates. This framework offers an alternative approach to improving manufacturing processes that involves utilising the power of I4.0 technologies in conjunction with lean manufacturing principles. This study’s examination of unprocessed data also provides guidance on improving legislation. The findings of this study provide direction for future research in the field of manufacturing and offer useful advice to businesses wishing to integrate I4.0 technologies into their operations.
Khurshid Alam, Sayyad Zahid Qamar, Muhammad Iqbal
et al.
Abstract Bone drilling is a universal procedure in orthopaedics for fracture fixation, installing implants, or reconstructive surgery. Surgical drills are subjected to wear caused by their repeated use, thermal fatigue, irrigation with saline solution, and sterilization process. Wear of the cutting edges of a drill bit (worn drill) is detrimental for bone tissues and can seriously affect its performance. The aim of this study is to move closer to minimally invasive surgical procedures in bones by investigating the effect of wear of surgical drill bits on their performance. The surface quality of the drill was found to influence the bone temperature, the axial force, the torque and the extent of biological damage around the drilling region. Worn drill produced heat above the threshold level related to thermal necrosis at a depth equal to the wall thickness of an adult human bone. Statistical analysis showed that a sharp drill bit, in combination with a medium drilling speed and drilling at shallow depth, was favourable for safe drilling in bone. This study also suggests the further research on establishing a relationship between surface integrity of a surgical drill bit and irreversible damage that it can induce in delicate tissues of bone using different drill sizes as well as drilling parameters and conditions.
Julian Frattini, Davide Fucci, Daniel Mendez
et al.
Context: Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Although decision-making during requirements engineering has significant consequences, the debt metaphor in requirements engineering is inadequately explored. Objective: We aim to conceptualize how the debt metaphor applies to requirements engineering by organizing concepts related to practitioners' understanding and managing of requirements engineering debt (RED). Method: We conducted two in-depth expert interviews to identify key requirements engineering debt concepts and construct a survey instrument. We surveyed 69 practitioners worldwide regarding their perception of the concepts and developed an initial analytical theory. Results: We propose a RED theory that aligns key concepts from technical debt research but emphasizes the specific nature of requirements engineering. In particular, the theory consists of 23 falsifiable propositions derived from the literature, the interviews, and survey results. Conclusions: The concepts of requirements engineering debt are perceived to be similar to their technical debt counterpart. Nevertheless, measuring and tracking requirements engineering debt are immature in practice. Our proposed theory serves as the first guide toward further research in this area.
Background: Software development results in the production of various types of artifacts: source code, version control system metadata, bug reports, mailing list conversations, test data, etc. Empirical software engineering (ESE) has thrived mining those artifacts to uncover the inner workings of software development and improve its practices. But which artifacts are studied in the field is a moving target, which we study empirically in this paper.Aims: We quantitatively characterize the most frequently mined and co-mined software artifacts in ESE research and the research purposes they support.Method: We conduct a meta-analysis of artifact mining studies published in 11 top conferences in ESE, for a total of 9621 papers. We use natural language processing (NLP) techniques to characterize the types of software artifacts that are most often mined and their evolution over a 16-year period (2004-2020). We analyze the combinations of artifact types that are most often mined together, as well as the relationship between study purposes and mined artifacts.Results: We find that: (1) mining happens in the vast majority of analyzed papers, (2) source code and test data are the most mined artifacts, (3) there is an increasing interest in mining novel artifacts, together with source code, (4) researchers are most interested in the evaluation of software systems and use all possible empirical signals to support that goal.
Abstract The current historic COVID-19 Pandemic moment has thrown into sharp relief the need for flexible and rigorous higher education that meets upskilling and reskilling needs of global workforces. Discussions of micro-credentialing predate the Pandemic but have received increased focus as potentially assisting in addressing perceived skills gaps. However, not all commentators have been complimentary about the possibilities inherent in micro-credentialing. In this paper we discuss Ralston (Postdigital Science and Education 3:83–101, 2021) criticism of the “microcredentialing craze” as provocation to consider how equitable, thoughtful and just educative aims may be met. We address Ralston’s argument that micro-credentials present an educative “moral hazard” by arguing that micro-credentialing will allow universities to respond quickly to changing worker educational needs rather than only offering full degrees that may not be economically viable or personally desirable for individuals. Rather, we suggest, the potential of micro-credentials lies in their pathways and potential to enhance lifelong learning and suggest that micro-credentials do not stand outside of the pedagogical ethical imperative that learning experiences should be positive and inclusive.
Special aspects of education, Information technology