Reviewerly/exHarmony huggingface dataset:
SwEDeL: Software Estimates’ Defense Lenses Designed from Negotiation Methods

Software organizations face increasing pressure for higher productivity and faster delivery. In this context, technically sound software estimates created by competent software practitioners to account for software risks can be rejected, favoring too aggressive estimates and the unrealistic commitments that arise from them. Then, software teams work under constant time pressure, sacrificing product quality to keep up with the expectations created. Time pressure also leads software practitioners to exhibit emotional distress, decreasing productivity, which leads to more time pressure and delays: a vicious cycle. This work proposes an approach to support software estimators in defending their realistic estimates instead of yielding to pressure over them. We designed a set of defense lenses based on consolidated negotiation principles and presented them through a digital simulation. We evaluated the digital simulation through a controlled experiment with software professionals. We employed the Theory of Planned Behavior to understand the intentions of participants to defend their software estimates, also collecting data on the antecedents of intentions: attitudes, subjective norms, and perceived behavioral control. Our results show that scores for the study variables improved among experimental group participants after participating in the digital simulation. They were more inclined to choose a defense action when facing pressure scenarios than the control group. The participants also perceived the lenses as useful, showing the relevance of the proposed approach. The original paper was published in Matsubara et al. [17].
A Systematic Mapping Study of the Onboarding Process in Software Development Organizations

The onboarding process refers to the organizational integration or socialization of new employees to the company and their role. This includes training, orientation, support, and communication, seeking to help newcomers to adapt to the new culture and work environment. Since software development is a human activity that requires collaboration and teamwork, especially in the professional environment, onboarding is a critical process to ensure the proper integration of new employees in software projects. Thus, this paper aims to identify the main approaches, practices, trends, and challenges related to the onboarding process in the Software Engineering context. We conducted a Systematic Mapping Study, which resulted in the selection of 23 primary studies. The main findings include the observation that onboarding process is context-sensitive, affected by characterisctics such as organization size, work model, and development methodologies. We also found that there is a lack of studies evaluating models and tools. Finally, there is a heavy focus on oboarding activities related to learning and socialization, in contrast to developing the confidence of newcomers.
Is your code harmful too? Understanding harmful code through transfer learning

Code smells are indicators of poor design implementation and decision-making that can potentially harm the quality of software. Therefore, detecting these smells is crucial to prevent such issues. Some studies aim to comprehend the impact of code smells on software quality, while others propose rules or machine learning-based approaches to identify code smells. Previous research has focused on labeling and analyzing code snippets that significantly impair software quality using machine learning techniques. These snippets are classified as Clean, Smelly, Buggy, and Harmful Code. Harmful Code refers to Smelly code segments that have one or more reported bugs, whether fixed or not. Consequently, the presence of a Harmful Code increases the risk of introducing new defects and/or design issues during the remediation process. While generating useful results for harmful code detection, none of the prior work has considered, through the use of transfer learning, train a model to identify harmful snippets in one programming language and being able to identify similar harmfulness in another programming language. We perform our study on this scope with 5 smell types, 258,035 versions of 23 open-source projects, 8,181 bugs and 11,506 code smells. The findings revealed promising transferability of knowledge between Java and C# in the presence of various code smells types, while C++ exhibited more challenging transferability. Also, our study discovered that a sample size of 32 demonstrated favorable outcomes for most harmful codes, underscoring the efficiency of transfer learning even with limited data.
On Building an Instrument to Assess the Organizational Climate of Agile Software Development Teams

Background: Measuring the organizational climate of agile teams is a challenge for practitioners, mainly due to the scarcity of specific instruments for the agile context. Not assessing specific human factors that contribute to the formation of agile the teams' climate inhibits the capacity for an assertive diagnosis, entailing difficulties for analyzing possible causes of problems and the execution of corrective actions within climate management. Objective: We aim to investigate how to support the organizational climate assessment of agile teams considering the influence of human factors. Method: Design Science Research (DSR) was used to develop TACT ("insTrument to Assess the organizational ClimaTe of agile teams"). We performed a Qualitative Study, a Tertiary Study, and a Systematic Literature Review to establish, refine, and delimit the DSR project elements. Three case studies were performed to evaluate and identify improvement points in TACT. Finally, a survey was conducted to validate TACT's factor structure. Result: We identified 38 human factors that influence the Teams' Climate. The psychometric properties of 19 climate measures representing more than 80 dimensions involving a variety of constructs were investigated. TACT was grounded in the most critical human factors that influence agile development. In the three case studies, we considered evidence from multiple sources, e.g., interviews, open-ended questions, reliability and validity indexes, and an evaluation using TAM (Technology Acceptance Model), to identify improvements and investigate the capability and quality of TACT to assess the teams' climate. Our latest investigation, the Validation Study, showed evidence of TACT theoretical structure plausibility. Conclusion: The results revealed that the TACT has good psychometric properties and provides professionals and researchers with a robust means to assess the ten critical dimensions for agility.
Enhancing Issue Management through the Employment of Inspection Technique: An Experience Report in The Industry

The software industry has been adopting the concept of Global Software Development to release products quicker and with fewer costs, assuring quality by employing software testing techniques which are able to detect issues early. In this context, this paper presents an experience report performed at SIDIA Institute of Science and Technology between the years of 2020 and 2022, during which a process of issue management (IM) was developed to reduce the problems in the issue report, such as: lack of cohesion, absence of confirmation tests and long learning curve time of the testing team. In order to face these challenges, the IM process employs inspection techniques in order to improve the quality of reports, and then, increase its effectiveness, i.e., the number of valid issues. In addition, to support the IM process, it was created an onboarding phase, a template for issue reports and the monitoring of issues until the confirmation test. Through the implementation of this process, there was evaluated a total amount of 11002 issues report, and as a result, it was observed that the issues' effectiveness rate was increased from to (raising by ) in the period in which this research was performed. In sequence, it was detected that around of issues had problems related to issue report writing. Furthermore, qualitative research revealed that of the interviewed testers consider the process effective. The authors hope that lessons learned described in this paper can contribute to the academic and industry communities by regarding the employment of inspection techniques to evaluate issue reports.
Variability Debt: A Multi-method Study

Technical debt is a metaphor to guide the identification, measurement, and general management of decisions that were mostly appropriate in the short term but created obstacles mainly for the evolution and maintenance of systems. Variability management, which is the ability to create variants of systems to satisfy different needs, is a potential source of technical debt. Variability debt, a term coined in this work, is caused by sub-optimal solutions in the implementation of variability management in software systems. We performed a systematic literature review to characterize variability debt, and conducted a field study in which we report quantitative and qualitative analysis based on documents (e.g., requirements, specifications, source code, and test cases) and a survey with stakeholders. The context is a large company with three different systems, where opportunistic reuse was applied to create variants for each system. We describe and characterize the variability debt phenomenon in this field study, and we assess the validity of the metaphor to create awareness in diverse company stakeholders and to guide technical debt management research related to variability aspects. The analysis of the field study's artifacts show evidences of factors that complicate the evolution of the variants, such as code duplication and non-synchronized artifacts. Time pressure is identified as the main cause for not considering other options than opportunistic reuse. Technical practitioners mostly agree on the creation of usability problems and complex maintenance of multiple independent variants. However, this is not fully perceived by managerial practitioners.
Evaluating the Code Comprehension of Novices with Eye Tracking

Code comprehension is essential for software evolution, but it can be hindered by structural problems in the code. Despite its importance, researchers often rely on static code metrics to investigate code comprehension, without considering dynamic aspects such as the visual effort involved. Therefore, we aim to fill this gap by examining code comprehension using eye-tracking metrics. We conducted three controlled eye-tracking studies with a total of 128 novices in three distinct programming languages: C, Python, and Java. We used constructions with different granularity levels, namely atoms of confusion, refactorings, and #ifdef annotations. With respect to atoms of confusion, we found that the clarified version of the code reduced the time and number of attempts by 38.6% and 28%, respectively. The use of the Extract Method refactoring significantly reduced the time of two tasks by 70% to 78.8%, while increasing the accuracy of three tasks by 20% to 34.4%. The use of #ifdef annotations presented reductions in the modified region by 46.9% in time, 44.7% in the fixation duration, 48.4% in the fixation count, and 60.5% in the regressions count. In addition, we also identified patterns in our data by observing several visual transitions going forward and backward between specific code elements which allowed us to infer confusion in the code, such as when adding the particle not before the variable. These results provide educators, practitioners, and researchers with valuable information on certain transformations and their potential to ease or hinder code comprehension. The study also highlights the potential of visual metrics to reveal the impact of transformations that cannot be captured by static code metrics. Our results also pave the way for the development of more advanced tools that track the eye movements of developers and assist them with helpful suggestions.
Surveying the Relevance of the Critical Success Factors of Agile Transformation Initiatives from a Project Management Perspective

Background: Agile methods and practices have been consistently adopted in recent years as alternatives to traditional software development processes to address the ever-changing needs of IT organizations. In a previous systematic mapping study, we identified twelve critical success factors of agile transformations from a project management perspective. Objective: In this paper, we investigate how practitioners perceive the relevance of these factors and whether other factors should be considered. Method: We conducted a survey research involving project managers from several organizations undergoing agile transformations. Results: The participants' perceptions provided valuable insights into the relevance of the critical success factors. Additionally, we identified five new critical success factors: organizational ambidexterity, use of tools and automation, breaking down organizational silos, team commitment, and alignment of organizational goals and expectations. These newly identified factors contribute to a more comprehensive understanding of organizations' challenges during an agile transformation. Based on the results and the literature, we formulated three propositions representing recommendations that can foster agile transformation. Conclusions: The evidence gathered in this study indicates that the factors investigated previously are highly relevant. Moreover, organizations should consider them to enhance the chances of success of agile transformation initiatives.
A Case Study to Evaluate the Introduction of Code Review in a Industrial Project

A software development team often follows a set of phases to ensure the product achieves its objectives. During these phases, it is essential to adopt good practices, such as code review, to ensure code quality. This paper reports a case study that aimed to evaluate the impact of the adoption of code review on a software project developed by an agile team at the Hospital Israelita Albert Einstein. We analyzed quantitative and qualitative metrics to compare two stages of the project: before and after the introduction of code review. After the introduction of such practices, we observed a trend of bug reduction and improvements regarding code quality. The discussions between authors and reviewers also improved. Additionally, the project team found the changes made in the project very positive.
Implementing a Simplified Risk Management Approach in Agile Software Development: An Experience Report

Agile software development projects often overlook risk management. Besides that, the lack of risk management could lead to problems such as delays, budget overruns, and even project termination, especially in distributed software development. In this paper, we address this topic in an industrial context. We implemented a simplified method to manage risks in distributed agile software development projects at Betabit company. The risk management approach proposed includes identifying, recording, and monitoring the risks faced by the company during its projects, enabling the creation of action plans to handle the risks. We assessed the acceptance of the method by applying the Technology Acceptance Model (TAM) questionnaire and open questions among the professionals who used it. The research results showed that the model was well accepted, with 90.1% of participants agreeing that it was useful in risk prevention and management. The benefits found were a broader view of projects, effective communication among teams, knowledge sharing, task clarification, and a structured approach to risk management. The challenging part is to learn how to use the method. Besides that, all respondents would recommend the proposed method to other companies, showing the potential of the method to manage risks in agile software projects.
ME-MPS: An MR-MPS-SW extension model for critical software in regulated environments

Safety-critical software puts human lives at risk or can cause significant financial losses in case of failures. Environments regulated by security standards impose requirements on critical software development organizations that aim to guide them in the development process. The MPS.br Reference Model for Software (MR-MPS-SW) aims to increase the quality of the software process of a wide range of Brazilian companies, small and medium-sized, from varied niches, being, therefore, a generalist model of high applicability and low application domain specificity. This work proposes the ME-MPS, an Extension Model to the MR-MPS-SW that adds Processes and Expected Results to guide entities interested in studying, adapting, or applying the national model in critical software projects. This work also reports the application in a proof of concept adaptation of one of the objectives of the RTCA DO-178C standard, used in software certification in the aviation domain.
The promise of training deep neural networks on CPUs: A survey

Abstract This survey presents a comprehensive analysis of the potential benefits and challenges of training deep neural networks (DNNs) on CPUs, summarizing existing research in the field. Five distinct DNN models are examined: Ternary Neural Networks (TNNs), Binary Neural Networks (BNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and a novel method called Sub-Linear Deep Learning Engine (SLIDE), specifically designed for CPU-based network training. The survey emphasizes the advantages of using CPUs for DNN training, such as low cost, compact size, and broad applicability across various domains. Furthermore, the survey collects concerns related to CPU acceleration, including the absence of a unified programming model and the inefficiencies in DNN training due to increased floating-point operations. The survey explores algorithmic and hardware optimization strategies, incorporating compressed network structures, innovative techniques like SLIDE, and the RISC-V instruction set to tackle these issues. According to the survey, CPUs are more likely to become the alternative for developers with limited resources in the future. Through continued algorithm optimization and hardware enhancements, CPUs can provide more cost-efficient neural network training solutions, excelling in areas such as mobile servers and edge computing.
A mechanical arm based on image recognition and remote control

Abstract Mechanical arm, image recognition and wireless transmission have been a hot topic and technology in recent years. This paper mainly introduces the object recognition mechanical arm based on the mechanical arm and wireless transmission technology. The system is modular, and the whole equipment system is divided into remote control system and mechanical arm system according to function. The remote control system is an upper computer software which integrates image recognition, related data processing module and wireless transmission module. The remote end includes wireless transmission module, steering gear control module and image acquisition module. The image is collected and sent to the remote control system, and the motion trajectory of the mechanical arm is obtained after calculation, and the control signal of the stepping motor on the mechanical arm is further calculated, and finally sent to the mechanical arm system to achieve the purpose of remote control. At the same time, during the movement of the mechanical arm, the relevant data is collected by the sensor on the mechanical arm and sent back to the remote control terminal for display and judgment. In this project, the mechanical arm system can automatically recognize the target on a two-dimensional plane and move the mechanical arm according to the object position. Meanwhile, the upper computer can display relevant data information.
Mobile robot navigation based on Deep Reinforcement Learning: A brief review

Abstract Navigation capacity is a key attribute of robot technology and the foundation for achieving other advanced behaviours. Compared to traditional navigation technology, applying Deep Reinforcement Learning (DRL) to artificial intelligence agents to achieve mobile robot navigation function is currently the academic focus. DRL is based on an end-to-end approach, transforming high-dimensional and continuous inputs into optimal policy to guide mobile robots, forming an advanced perceptual control system. In this article, DRL is first compared with traditional navigation technology and SLAM, and its application advantages are elucidated. Then, the basic background and classic algorithm models of standard reinforcement learning and DRL are systematically elaborated. Finally, the application of DRL in different application scenarios and research fields is introduced.
Research on the application of transformer in computer vision

Abstract The Transformer is a deep neural network model that utilizes attention mechanisms to improve model performance. Initially, the Transformer gained significant attention in the field of natural language processing. In recent years, due to continuous improvements and extensions to the Transformer model structure, it has also achieved many important breakthroughs in computer vision(CV) tasks, attracting the interest of many researchers. However, there is a lack of comprehensive review articles on the application and development of the Transformer in computer vision. A summary of the Transformer’s applications and advancements in computer vision is given in this paper. It discusses the Transformer model’s fundamental ideas and organizational framework, and primarily introduces its applications in various fields such as image classification, object detection, and image generation, as well as the superiority of the Transformer+ convolutional neural network(CNN) fusion model. The paper provides a detailed analysis of classic models such as Vision Transformer(ViT), Detection Transformer(DETR) and discusses their strengths, weaknesses, and improvement methods. Finally, the paper summarizes and looks forward to the Transformer’s evolution in computer vision.
Peer Review Declaration

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Single anonymous • Conference submission management system: Morressier • Number of submissions received: 75 • Number of submissions sent for review: 69 • Number of submissions accepted: 47 • Acceptance Rate (Submissions Accepted / Submissions Received × 100): 62.6 % • Average number of reviews per paper: 1.00 • Total number of reviewers involved: 5 • Contact person for queries: Name: Dimas Danar Dewa Affiliation: Universitas Diponegoro Email: dwdanar@gmail.com * means value has been edited
Comparison and summary of Faster R-CNN, YOLOv3 and YOLOv5 applied in vehicle detection

Abstract Nowadays, computer vision and machine learning are widely applied in vehicle detection. The technique of object detection is also of necessity in this realm. However, due to the large variety of models in object detection, it is important to find the most specifically suitable model for detecting vehicles in many situations. In this paper, we focus on comparing and summarizing Faster R-CNN, YOLOv3 and YOLOv5 applied in vehicle detection. We would introduce the models in relative detail and design an experiment to verify the models’ performances. The methodology for the experiment is to train the three models using the same dataset of vehicles, compare the different attributes of these results and find the most suitable application scenario for vehicle detection for each model.