Search

Cell rearrangements are fundamental mechanisms driving large-scale deformations of living tissues. In three-dimensional (3D) space-filling cell aggregates, cells rearrange through local topological transitions of the network of cell-cell interfaces, which is most conveniently described by the vertex model. Since these transitions are not yet mathematically properly formulated, the 3D vertex model is generally difficult to implement. The few existing implementations rely on highly customized and complex software-engineering solutions, which cannot be transparently delineated and are thus mostly non-reproducible. To solve this outstanding problem, we propose a reformulation of the vertex model. Our approach, called Graph Vertex Model (GVM), is based on storing the topology of the cell network into a knowledge graph with a particular data structure that allows performing cell-rearrangement events by simple graph transformations. Importantly, when these same transformations are applied to a two-dimensional (2D) polygonal cell aggregate, they reduce to a well-known T1 transition, thereby generalizing cell-rearrangements in 2D and 3D space-filling packings. This result suggests that the GVM's graph data structure may be the most natural representation of cell aggregates and tissues. We also develop a Python package that implements GVM, relying on a graph-database-management framework Neo4j. We use this package to characterize an order-disorder transition in 3D cell aggregates, driven by active noise and we find aggregates undergoing efficient ordering close to the transition point. In all, our work showcases knowledge graphs as particularly suitable data models for structured storage, analysis, and manipulation of tissue data. Author summary: Space-filling polygonal and polyhedral packings have been studied as physical models for foams and living tissues for decades. One of the main challenges in the field is to mathematically describe complex topological transformations of the network of cell-cell interfaces that are present during cell rearrangements, accompanying plastic deformations and large-scale cellular flows. Our work addresses this challenge by storing the topology of the network of cell-cell interfaces into a knowledge graph with a specific data structure, uniquely defined by a metagraph. It turns out that this graph technology, also used by tech giants such as Google and Amazon, allows representing topological transformations as graph transformations, that are intuitive, easy to visualize, and straight-forward to implement computationally. [ABSTRACT FROM AUTHOR]

We deal with no-signaling correlations that include Bell-type quantum nonlocality. We consider a logical implementation using a trusted central server with encrypted connections to clients. We show that in this way it is possible to implement two-party no-signaling correlations in an asynchronous manner. While from the point of view of physics our approach can be considered as the computer emulation of the results of measurements on entangled particles, from the software engineering point of view it introduces a primitive in communication protocols that can be capable of coordinating agents without revealing the details of their actions. We present an actual implementation in the form of a Web-based application programming interface (RESTful Web API). We demonstrate the use of the API via the simple implementation of the Clauser–Horne–Shimony–Holt game. [ABSTRACT FROM AUTHOR]

The weather and climate model ICON (ICOsahedral Nonhydrostatic) is being used in high resolution climate simulations, in order to resolve small-scale physical processes. The envisaged performance for this task is 1 simulated year per day for a coupled atmosphere-ocean setup at global 1.2 km resolution. The necessary computing power for such simulations can only be found on exascale supercomputing systems. The main question we try to answer in this article is where to find sustained exascale performance, i. e. which hardware (processor type) is best suited for the weather and climate model ICON and consequently how this performance can be exploited by the model, i. e. what changes are required in ICON's software design so as to utilize exascale platforms efficiently. To this end, we present an overview of the available hardware technologies and a quantitative analysis of the key performance indicators of the ICON model on several architectures. It becomes clear that domain decomposition-based parallelization has reached the scaling limits, leading us to conclude that the performance of a single node is crucial to achieve both better performance and better energy efficiency. Furthermore, based on the computational intensity of the examined kernels of the model it is shown that architectures with higher memory throughput are better suited than those with high computational peak performance. From a software engineering perspective, a redesign of ICON from a monolithic to a modular approach is required to address the complexity caused by hardware heterogeneity and new programming models to make ICON suitable for running on such machines. [ABSTRACT FROM AUTHOR]

Visual content in the form of images and videos has taken over general‐purpose social networks in a variety of ways, streamlining and enriching online communications. We are interested to understand if and to what extent the use of images is popular and helpful in social coding platforms. We mined 9 years of data from two popular software developers' platforms: the Mozilla issue tracking system, that is, Bugzilla, and the most well‐known platform for developers' Q/A, that is, Stack Overflow. We further triangulated and extended our mining results by performing a survey with 168 software developers. We observed that, between 2013 and 2022, the number of posts containing image data on Bugzilla and Stack Overflow doubled. Furthermore, we found that sharing images makes other developers engage more and faster with the content. In the majority of cases in which an image is included in a developer's post, the information in that image is complementary to the text provided. Finally, our results showed that when an image is shared, understanding the content without the information in the image is unlikely for 86.9% of the cases. Based on these observations, we discuss the importance of considering visual content when analyzing developers and designing automation tools. [ABSTRACT FROM AUTHOR]

Requirements collection is difficult and a critical phase of software development life cycle, in particular in a global software development (GSD) environment. In GSD, clients and vendors are physically separated such that there exist challenges such as lack of face‐to‐face communication, language differences, culture variation, and time zone differences. The objective of current research is to identify critical challenges of requirement engineering in GSD. There is no work done yet to empirically analyze all possible challenges via questionnaire survey from software industries. This research paper empirically investigates and analyze the identified challenges from systematic literature review (SLR) based on a questionnaire survey. For this purpose, 50 respondents from different countries are organized. From this research, 13 challenges during requirements implementation in the context of GSD have been identified previously via SLR. These challenges were then evaluated using an empirical approach of questionnaire survey. The results from respondents were analyzed based on type of respondents, level of experience of respondents, and from client–vendor perspective. Finally, the challenges were prioritized based on its frequency of occurrences from the SLR and the questionnaire survey. The relationships between the challenges and the survey results were evaluated using the Spearman's correlation coefficient. The results produced a 0.835 Spearman's correlation coefficient at significance level ρ = 0.000, which showed a strong positive correlation between the outcome of SLR and survey with no significant difference. The implication of this research work lies in both fundamental and practical perspective. The prioritized set of challenges was provided based on SLR, and questionnaire survey acts as a knowledge base for both researchers and industrial practitioners. This work will help researchers to identify challenges in GSD projects or other software engineering areas. [ABSTRACT FROM AUTHOR]

The objective of this study is to analyze the current perspective as regards knowledge related to what causes stress or motivates developers, how these two aspects are related to each other, and how this in turn affects their performance in the sphere of Global Software Development and how these can be controlled. This paper presents the results obtained after conducting a systematic mapping study of literature in order to analyze how stress, motivation, and performance affect the project members in Global Software Development teams. We carried out a systematic mapping of published studies dealing with stress, motivation, and performance in global software engineering. A total of 118 papers dealing with this subject were found. The literature analyzed provided a relatively significant quantity of data referring to the impact that the characteristics of distributed software development projects have on the performance and productivity of teams, along with the actions taken to improve that performance. However, when focusing on the analysis of the impact of this type of projects on team members' motivation, and on the actions that can be taken to improve that motivation, we discovered that the number of works decreases considerably and that works referring to the impact of this kind of development on developers' stress were virtually non‐existent, as were those concerning ways in which to improve that stress. We are, therefore, of the opinion that it is necessary to carry out in‐depth research into the aspects of working in distributed teams that may have a negative impact on developers' levels of motivation and stress, along with what could be beneficial in order to improve levels of motivation and decrease levels of stress. [ABSTRACT FROM AUTHOR]

In the field of software engineering, graph‐based models are used for a variety of applications. Usually, the layout of those graphs is determined at the discretion of the user. This article empirically investigates whether different layouts affect the comprehensibility or popularity of a graph and whether one can predict the perception of certain aspects in the graph using basic graphical laws from psychology (i.e., Gestalt principles). Data on three distinct layouts of one causal graph is collected from 29 subjects using eye tracking and a print questionnaire. The evaluation of the collected data suggests that the layout of a graph does matter and that the Gestalt principles are a valuable tool for assessing partial aspects of a layout. [ABSTRACT FROM AUTHOR]

Software engineers widely acknowledge the inclusion of security requirements in the early stages of the development process. However, the need to prepare the software for the failure of the implemented security controls and subsequent investigation of the incident is often not discussed. Forensic‐ready software systems represent an evolution of secure systems being designed for the eventual digital forensic investigation. However, their exact properties remain largely unexplored, beyond preliminary high‐level conceptualizations of requirements and capabilities. Further obstacles hindering the adoption of forensic‐ready software systems are the different priorities and goals of involved parties and a gap in the digital forensics expertise of software engineers. In this paper, we conduct an empirical qualitative study identifying the problems and needs of forensic readiness while framing the notion of an ideal forensic‐ready software system and how it should treat potential evidence. To this end, we conducted semisupervised interviews with digital forensics experts on their idea, experience, and suggestions. The results provide insights into the needs of the experts to facilitate the definition of correct requirements towards forensic‐ready software systems to support the anticipated investigations properly. [ABSTRACT FROM AUTHOR]

Software effort estimation is a vital process in the software industry for successfully administering 5Ds of the software development life cycle (SDLC). The 5Ds stand for demand, development, direction, deployment, and designated cost of the software. Software development effort estimation (SDEE) is an effort prediction mechanism to calculate the effort for the development of the software product in order to minimize the challenges in the software field. Academics and practitioners are striving to identify which machine learning estimation technique yields more accurate results based on evaluation metrics, datasets, and other pertinent aspects. The feature selection techniques impact accuracy by selecting the main and relevant features in the dataset and eliminating the redundant and irrelevant features in the dataset. To achieve accurate estimations, the paper utilizes feature selection algorithms, along with various machine learning techniques, which predict the desired effort and the performance of the model has been measured in terms of prediction accuracy, R2 value, relative error, and mean absolute error. The datasets China and Maxwell are trained with the relevant features by applying feature selection algorithms, and estimation techniques are applied to predict the effort. The performance is compared with the regression models and feature selection techniques utilized by many authors previously. The result of the proposed methodology significantly gives the best performance with the combination of feature selection and estimation models than all regression models when applied alone, to both datasets. From the results, it is perceptible that random forest is performing well with the feature selection techniques and obtains the highest prediction accuracy of 99.33% with the China and 89.47% with the Maxwell datasets. [ABSTRACT FROM AUTHOR]

In modern times, internationally organized teams face a number of coordination problems owing to their different physical operating locations. These challenges usually come in temporal, cultural, and linguistic forms. To resolve some of these issues, we need more coordination, teamwork, and shared understanding in the requirements engineering phase. Many approaches have been introduced to overcome these challenges associated with global software engineering (GSE). The objective of this research study is to introduce amateurs to GSE and improve their understanding of its associated challenges through an activity‐based learning approach. Our method is primarily targeted toward students who already have theoretical knowledge on the topic but require first‐hand experience with GSE. With the aforementioned motivation in mind, we propose, designe, and empirically evaluate two different activities that can help enhance awareness of GSE challenges. For each activity, we simulate an environment wherein participants are made to go through various constructed coordination challenges related to communication, time management, team mistrust, linguistic barriers, cultural barriers, and distribution of tasks. The effectiveness of our proposed activities, captured by the extent to which participants were able to deal with GSE challenges, was judged through various techniques including (i) observation, (ii) post activities survey questionnaire, and (iii) brainstorming and discussion. We show that the proposed activities were effective in helping students learn and further their understanding of GSE concepts. In particular, discussion sessions and survey questionnaire results reflect their ability to identify critical GSE challenges (specifically related to teams) in a simulated scenario. [ABSTRACT FROM AUTHOR]

When it comes to software development, precise planning, proper documentation and proper process control, some errors are inevitable in the software environment. These software flaws can lead to quality deterioration, which can be the main reason behind system failure. As the whole world especially developing countries is dependent upon software systems, it is very important to focus on its reliability aspect. Nowadays sophisticated systems require concerted efforts for managing and reducing the shortcomings in software engineering. But, these efforts require more cost, more money and more time. Software error prediction is the most helpful step in the testing stage of the software development life cycle. It identifies components or parts of the code where an error may occur and requires broad testing, so the test resources can be efficiently used. Software error assessment reduces efforts of testing the software by helping the software testers locate the actual problem and classify different classes of errors in the system. Error estimators are majorly used in various organizations to evaluate the software to save time, improve the quality of software and testing and optimize resources to meet timelines. Machine learning provides support in fault projection by collecting the training data from various edge devices and thus helps in escalating the reliability of the software available on Kaggle. The multilayer perceptron shows better results in precision, recall, F1 score and accuracy as compared to decision tree and Gaussian Naive Bayes as it achieves an accuracy of 96.8%. [ABSTRACT FROM AUTHOR]

The development of smart and innovative software applications in various disciplines has inspired our lives by providing various cutting-edge technologies spanning from online to smart and efficient systems. The proliferation of innovative internet-enabled tools has transformed the nation into a globalized world where individuals can participate on various platforms, collaborate in activities, communicate on issues, and exchange information safely and consistently. Coordination and cooperation are essential in software development. It gathers all software developers in one space, encouraging them to discuss goals and work rationally to accomplish the project goal. In recent years, continuous software development and deployment have become increasingly common in software engineering. Continuous software engineering (CSE) is a method that involves a variety of strategies to increase the regularity of novel and modified software versions. CSE enables a continuous learning and improvement process through rapid software update iteration by combining continuous integration and delivery. Continuous integration is a method that has arisen in order to remove gaps between development and deployment. Software engineers must handle uncertainty and alter stakeholders' requirements, which is possible through continuous software developmental strategies that manage the overall software cycle and produce high-quality software applications. The proposed study is a systematic review related to continuous software development and deployment and focuses on achieving four aims: (1) To explore the impacts of continuous development on software, (2) to pinpoint various tools used to carry out this process, (3) to highlight the challenges faced in adopting continuous approaches for development and (4) to analyze the phases of continuous software engineering. [ABSTRACT FROM AUTHOR]

Interoperability stands as a critical hurdle in developing and overseeing distributed and collaborative systems. Thus, it becomes imperative to gain a deep comprehension of the primary obstacles hindering interoperability and the essential criteria that systems must satisfy to achieve it. In light of this objective, in the initial phase of this research, we conducted a survey questionnaire involving stakeholders and practitioners engaged in distributed and collaborative systems. This effort resulted in the identification of eight essential interoperability requirements, along with their corresponding challenges. Then, the second part of our study encompassed a critical review of the literature to assess the effectiveness of prevailing conceptual approaches and associated technologies in addressing the identified requirements. This analysis led to the identification of a set of components that promise to deliver the desired interoperability by addressing the requirements identified earlier. These elements subsequently form the foundation for the third part of our study, a reference architecture for interoperability-fostering frameworks that is proposed in this paper. The results of our research can significantly impact the software engineering of interoperable systems by introducing their fundamental requirements and the best practices to address them, but also by identifying the key elements of a framework facilitating interoperability in Systems of Systems. [ABSTRACT FROM AUTHOR]

Software maintenance and evolution play an important role in the software engineering field, especially when current software becomes more and more complex and powerful. As an entity to implement business processes and gain revenue, valuable software is composed of business logic and corresponding organization role interaction interfaces. With the enterprise development, the organization architecture also evolves, like expanding, cross department cooperation, and so on. However, existing software process adaptive approaches mainly focus on handling the change of the business (program) logic instead of organization structure. Therefore, we propose an adaptive software business process approach that supports organization architecture evolution and automatically migrates the run‐time process instances to the latest version. First, a business process adaptation model is designed, which includes the organization layer, business process layer and event layer that connects the two. Based on the model, the organization changing impact and business process model modification are formalized. Besides, the business process adaptation approach is designed. According to the dependence between the organization architecture and the business process activities, the affected domain detection algorithms for three basic business process structures and the business process instance migration algorithm are developed. Finally, the feasibility and stability of the proposed system are comprehensively evaluated with the synthetic data sets. [ABSTRACT FROM AUTHOR]

Sentiment analysis is becoming increasingly important in today's digital age, with social media being a significant source of user-generated content. The development of sentiment lexicons that can support languages other than English is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existing sentiment analysis systems focus on English, leaving a significant research gap in other languages due to limited resources and tools. This research aims to address this gap by building a sentiment lexicon for local languages, which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexicon is developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentiment scores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. In the second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis of Roman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrieval metrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The results showcase the potential for improving software engineering tasks related to user feedback analysis and product development. [ABSTRACT FROM AUTHOR]

In this research, a microlearning strategy for Software Engineering supported by a mobile application was designed and implemented. The goal is to evaluate the motivation and learning outcomes in the specific context of Software Project Management, with the Scrum framework, in participants of a Software Engineering course at a Latin American higher education institution. An empirical investigation was conducted using a quantitative approach, a quasi‐experimental design, and pretest–posttest measurements without a control group. A one‐sample t‐test for comparison of the means of a sample was used. Statistically significant differences were found between the theoretical and empirical mean of the variable motivation to learn in the specific context and the variable Stimulus for learning after interacting with the mobile application. The means were higher than the theoretical average of the scale, which suggests that the participants valued the mobile application positively. Regarding the learning outcomes of the Scrum framework, a paired sample t‐test for comparison of means revealed an increase in posttest scores, although this rise was not statistically significant. Microlearning can increase the participants' motivation and promote learning in the specific context of Software Project Management. The mobile application has the potential to support microlearning since the participants felt highly motivated and agreed that its use facilitates learning, a key aspect of success in a microlearning strategy. [ABSTRACT FROM AUTHOR]

One primary objective of drone simulation is to evaluate diverse drone configurations and contexts aligned with specific user objectives. The initial challenge for simulator designers involves managing the heterogeneity of drone components, encompassing both software and hardware systems, as well as the drone's behavior. To facilitate the integration of these diverse models, the Functional Mock-Up Interface (FMI) for co-simulation proposes a generic data-oriented interface. However, an additional challenge lies in simplifying the configuration of co-simulation, necessitating an approach to guide the modeling of parametric features and operational conditions such as failures or environment changes. The article addresses this challenge by introducing CARES, a model-driven engineering and component-based approach for designing drone simulators, integrating the FMI for co-simulation. The proposed models incorporate concepts from component-based software engineering and FMI. The NAVIDRO architectural style is presented for designing and configuring drone co-simulation. CARES utilizes a code generator to produce structural glue code (Java or C++), facilitating the integration of FMI-based domain-specific code. The approach is evaluated through the development of a simulator for navigation functions in an autonomous underwater vehicle, demonstrating its effectiveness in assessing various autonomous underwater vehicle configurations and contexts. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive survey on test optimization in deep neural network (DNN) testing. Here, test optimization refers to testing with low data labeling effort. We analyzed 90 papers, including 43 from the software engineering (SE) community, 32 from the machine learning (ML) community, and 15 from other communities. Our study: (i) unifies the problems as well as terminologies associated with low-labeling cost testing, (ii) compares the distinct focal points of SE and ML communities, and (iii) reveals the pitfalls in existing literature. Furthermore, we highlight the research opportunities in this domain. [ABSTRACT FROM AUTHOR]

Software engineering (SE) requires developers to collaborate with stakeholders, and understanding their emotions and perspectives is often vital. Empathy is a concept characterising a person's ability to understand and share the feelings of another. However, empathy continues to be an under-researched human aspect in SE. We studied how empathy is practised between developers and end users using a mixed methods case study. We used an empathy test, observations, and interviews to collect data and socio-technical grounded theory and descriptive statistics to analyse data. We identified the nature of awareness required to trigger empathy and enablers of empathy. We discovered barriers to empathy and a set of potential strategies to overcome these barriers. We report insights on emerging relationships and present a set of recommendations and potential future works on empathy and SE for software practitioners and SE researchers. [ABSTRACT FROM AUTHOR]

Models such as finite state automata are widely used to abstract the behavior of software systems by capturing the sequences of events observable during their execution. Nevertheless, models rarely exist in practice and, when they do, get easily outdated; moreover, manually building and maintaining models is costly and error-prone. As a result, a variety of model inference methods that automatically construct models from execution traces have been proposed to address these issues. However, performing a systematic and reliable accuracy assessment of inferred models remains an open problem. Even when a reference model is given, most existing model accuracy assessment methods may return misleading and biased results. This is mainly due to their reliance on statistical estimators over a finite number of randomly generated traces, introducing avoidable uncertainty about the estimation and being sensitive to the parameters of the random trace generative process. This article addresses this problem by developing a systematic approach based on analytic combinatorics that minimizes bias and uncertainty in model accuracy assessment by replacing statistical estimation with deterministic accuracy measures. We experimentally demonstrate the consistency and applicability of our approach by assessing the accuracy of models inferred by state-of-the-art inference tools against reference models from established specification mining benchmarks. [ABSTRACT FROM AUTHOR]

Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents to software engineering researchers the six key challenges that a domain expert faces in addressing their problem with a computational workflow, and the underlying executable implementation. These challenges arise out of our conceptual framework which presents the "route" of transformations that a domain expert may choose to take while developing their solution. To ground our conceptual framework in the state of the practice, this article discusses a selection of available textual and graphical workflow systems and their support for the transformations described in our framework. Example studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain specificity and machine learning usage of their problem, workflow, and implementation. The state of the practice informs our discussion of the six key challenges, where we identify which challenges and transformations are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains. [ABSTRACT FROM AUTHOR]

Capturing users' engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collecting and analyzing users' feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in contexts where online feedback is limited, as for the majority of apps, and software in general. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this article, we propose to utilize biometric data, in terms of physiological and voice features, to complement product feedback interviews with information about the engagement of the user on product-relevant topics. We evaluate our approach by interviewing users while gathering their physiological data (i.e., biofeedback) using an Empatica E4 wristband, and capturing their voice through the default audio-recorder of a common laptop. Our results show that we can predict users' engagement by training supervised machine learning algorithms on biofeedback and voice data, and that voice features alone can be sufficiently effective. The best configurations evaluated achieve an average F1 ∼ 70% in terms of classification performance, and use voice features only. This work is one of the first studies in requirements engineering in which biometrics are used to identify emotions. Furthermore, this is one of the first studies in software engineering that considers voice analysis. The usage of voice features can be particularly helpful for emotion-aware feedback collection in remote communication, either performed by human analysts or voice-based chatbots, and can also be exploited to support the analysis of meetings in software engineering research. [ABSTRACT FROM AUTHOR]

Summary: End‐user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. Software researchers and vendors started to mine end‐user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high‐star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end‐users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end‐user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end‐users and professionals use to report fake reviews by critically analyzing the end‐user feedback. We conducted a detailed survey at the University of Science and Technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd‐users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end‐users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end‐user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f‐measures. The accuracy of manually identifying fake reviews by the crowd‐users is 44.4%. In contrast, the M&DL classifiers obtained an average accuracy of 96%. The experimental results obtained with various M&DL classifiers are encouraging. It is the first step towards identifying fake reviews in the app store by studying its implications in software and requirements engineering. [ABSTRACT FROM AUTHOR]

Context: The COVID‐19 pandemic has had a disruptive impact on how people work and collaborate across all global economic sectors, including software business. While remote working is not new for software engineers, forced WFH situations come with both limitations and opportunities. As the 'new normal' for working might be based on the current state of Work‐from‐home (WFH), it is useful to understand what has happened and learn from that. Objective: This study aims to gain insights into how their WFH arrangement impacts project management and software engineering. We are also interested in exploring these impacts in different contexts, such as startups and established companies. Method: We conducted a global‐scale, cross‐sectional survey during the spring and summer 2021. Our results are based on quantitative and qualitative analysis of 297 valid responses. Results: We characterize the profile of WFH in both spatial and temporal aspects, together with a set of common collaborative tools and coordination and control mechanisms. We revealed some areas of project management that are relatively more challenging during WFH situations, such as coordination, communication and project planning. We also revealed a mixed picture of the perceived impact of WFH on different software engineering activities. Conclusion: WFH is a situational phenomenon which can have both negative and positive impact on software teams. For practitioners, we suggest a unified approach to consider the context of WFH, collaborative tools, associated coordination and control approaches and a process that resolve those aspects that are sensitive to physical interaction. [ABSTRACT FROM AUTHOR]

Numerous academic research projects and industrial tasks related to software engineering require individual requirements as input. Unfortunately, according to our observation, several requirements may be packed in one paragraph without explicit boundaries in specification documents. To understand this problem's prevalence, we performed a preliminary study on the open requirement documents widely used in the academic community over the last 10 years, and found that 26% of them include this phenomenon. Several text segmentation approaches have been reported; however, they tend to identify topically coherent units which may contain more than one requirement. What is more, they do not take the constitutions of semantic units of requirements into consideration. Here we report a two‐phase learning‐based approach named DRIP to segment individual requirements from paragraphs. To be specific, we first propose a Requirement Segmentation Siamese framework, which models the similarity of sentences and their conjunction relations, and then detects the initial boundaries between individual requirements. Then, we optimize the boundaries heuristically based on the semantic completeness validation of the segments. Experiments with 1132 paragraphs and 6826 sentences show that DRIP outperforms the popular unsupervised and supervised text segmentation algorithms with respect to processing different documents (with accuracy gains of 57.65%–187.53%) and processing paragraphs of different complexity (with average accuracy gains of 54.46%–158.68%). We also show the importance of each component of DRIP to the segmentation. [ABSTRACT FROM AUTHOR]

This study was carried out with the aim of developing a software program that will enable the breeder to decide easily during the preparation of the feasibility for livestock enterprises. For this purpose, 63 enterprises in Gaziantep and Sanliurfa provinces/Turkey were visited between the years 2021-2022 and all the data obtained were evaluated. The "C#" programming language was used in the development of the software program. "Microsoft SQL Server" database was used to store the obtained data. This feasibility program is a software program where productivity checks are performed for enterprises and their personnel. It is a program that can be used easily from a small-capacity enterprises to a large-capacity enterprises. The cost calculations are not included in the program due to the economic conditions of the market. As a result, this program, which was prepared by taking into account software engineering techniques, will provide great advantages and conveniences for enterprises. Risk factors will be determined and alternatives will be presented with this software program that performs enterprises efficiency testing. It has been concluded that this software will be a program that can be preferred by the breeder since it can be used on all computers and offers different alternatives in enterprises establishments. [ABSTRACT FROM AUTHOR]

As code is now an inextricable part of science it should be supported by competent Software Engineering, analogously to statistical claims being properly supported by competent statistics. If and when code avoids adequate scrutiny, science becomes unreliable and unverifiable because results — text, data, graphs, images, etc — depend on untrustworthy code. Currently, scientists rarely assure the quality of the code they rely on, and rarely make it accessible for scrutiny. Even when available, scientists rarely provide adequate documentation to understand or use it reliably. This paper proposes and justifies ways to improve science using code: 1.   Professional Software Engineers can help, particularly in critical fields such as public health, climate change and energy. 2.   'Software Engineering Boards,' analogous to Ethics or Institutional Review Boards, should be instigated and used. 3.   The Reproducible Analytic Pipeline (RAP) methodology can be generalized to cover code and Software Engineering methodologies,        in a generalization this paper introduces called RAP +. RAP + (or comparable interventions) could be supported and or even required       in journal, conference and funding body policies. The paper's Supplemental Material provides a summary of Software Engineering best practice relevant to scientific research, including further suggestions for RAP + workflows. 'Science is what we understand well enough to explain to a computer.' Donald E. Knuth in |$A=B$| [ 1 ] 'I have to write to discover what I am doing.' Flannery O'Connor, quoted in Write for your life [ 2 ] 'Criticism is the mother of methodology.' Robert P. Abelson in Statistics as Principled Argument [ 3 ] 'From its earliest times, science has operated by being open and transparent about methods and evidence, regardless of which technology has been in vogue.' Editorial in Nature [ 4 ] [ABSTRACT FROM AUTHOR]

The rapid evolution of video programming tutorials as a key educational resource has highlighted the need for effective code extraction methods. These tutorials, varying widely in video quality, present a challenge for accurately transcribing the embedded source code, crucial for learning and software development. This study investigates the impact of video quality on the performance of optical character recognition (OCR) engines and the potential of large language models (LLMs) to enhance code extraction accuracy. Our comprehensive empirical analysis utilizes a rich dataset of programming screencasts, involving manual transcription of source code and the application of both traditional OCR engines, like Tesseract and Google Vision, and advanced LLMs, including GPT-4V and Gemini. We investigate the efficacy of image super-resolution (SR) techniques, namely, enhanced deep super-resolution (EDSR) and multi-scale deep super-resolution (MDSR), in improving the quality of low-resolution video frames. The findings reveal significant improvements in OCR accuracy with the use of SR, particularly at lower resolutions such as 360p. LLMs demonstrate superior performance across all video qualities, indicating their robustness and advanced capabilities in diverse scenarios. This research contributes to the field of software engineering by offering a benchmark for code extraction from video tutorials and demonstrating the substantial impact of SR techniques and LLMs in enhancing the readability and reusability of code from these educational resources. [ABSTRACT FROM AUTHOR]

Enterprise architecture is a growing trend that aims to help deal with the complexity of socio‐technical systems such as human organizations, as well as their information technology and systems areas. Nevertheless, the contribution of enterprise architecture to the field of software engineering remains unclear. The purpose of this systematic literature review is to see how enterprise architecture is used in software development and maintenance practice. To this end, we first carried out a search in the SCOPUS database and then organized the papers according to the Software Engineering Body of Knowledge to determine what areas of software engineering are covered by each research study. To understand how enterprise architecture is used, we established a classification based on ISO 42010 and TOGAF. From the systematic literature review, we noticed that the early stages of development are the most impacted by the enterprise architecture. On the other hand, we observed that enterprise architecture is of assistance in the areas of engineering management, engineering processes, and engineering models and methods; these tasks are carried out by teams or managers using different, often agile, development methods or standards. In turn, we found that the most common categories are architecture descriptions; these are often used to facilitate communication and information‐sharing between different stakeholders, in addition to frameworks, which will help to establish common practices in the organization related to the joint use of enterprise architecture and software development. [ABSTRACT FROM AUTHOR]

Technical debt (TD) is a metaphor used to refer to the added software system costs acquired from taking shortcuts. Unfortunately, large amounts of TD can lead to serious consequences, and, thus, the management of TD is essential. Due to TD being a relatively new subject of study, many aspects of TD remain ambiguous. Fortunately, Twitter has been proven to hold a wealth of information on many subjects. As such, this survey study aims to gain a better understanding on how interest in TD has evolved over time and how TD is addressed on Twitter. A total of 128,897 TD‐related tweets were scrapped from Twitter and analyzed using a number of proxy measures and Latent Dirichlet Allocation (LDA). The results revealed that interest in TD on Twitter has been generally increasing since the platform's early stages. Furthermore, TD‐related tweets were found to revolve around 11 distinct categories. The TD in games category was discovered to be the most popular category, followed by TD communication and TD repayment. The results highlight that TD is a diverse and overarching topic that contains many potential avenues for further exploration. Software engineering researchers, practitioners, and educators can utilize this study to help steer their TD‐related future efforts. [ABSTRACT FROM AUTHOR]

Copyright of Roca: Revista Científico-Educacional de la Provincia de Granma is the property of Universidad de Granma, Departamento Editorial 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.)

Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards. [ABSTRACT FROM AUTHOR]

There has been a recent surge of interest in automating software engineering tasks using deep learning. This article addresses the problem of code generation, in which the goal is to generate target code given source code in a different language or a natural language description. Most state-of-the-art deep learning models for code generation use training strategies primarily designed for natural language. However, understanding and generating code requires a more rigorous comprehension of the code syntax and semantics. With this motivation, we develop an encoder-decoder Transformer model in which both the encoder and decoder are explicitly trained to recognize the syntax and dataflow in the source and target codes, respectively. We not only make the encoder structure aware by leveraging the source code's syntax tree and dataflow graph, but we also support the decoder in preserving the syntax and dataflow of the target code by introducing two novel auxiliary tasks: Abstract Syntax Tree (AST) path prediction and dataflow prediction. To the best of our knowledge, this is the first work to introduce a structure-aware Transformer decoder that models both syntax and dataflow to enhance the quality of generated code. The proposed StructCoder model achieves state-of-the-art performance on code translation and text-to-code generation tasks in the CodeXGLUE benchmark and improves over baselines of similar size on the APPS code generation benchmark. Our code is publicly available at https://github.com/reddy-lab-code-research/StructCoder/. [ABSTRACT FROM AUTHOR]

Sentiment analysis of domain-specific short messages (DSSMs) raises challenges due to their peculiar nature, which can often include field-specific terminology, jargon, and abbreviations. In this paper, we investigate the distinctive characteristics of user-generated content across multiple domains, with DSSMs serving as the central point. With cross-domain models on the rise, we examine the capability of the models to accurately interpret hidden meanings embedded in domain-specific terminology. For our investigation, we utilize three different community platform datasets: a Jira dataset for DSSMs as it contains particular vocabulary related to software engineering, a Twitter dataset for domain-independent short messages (DISMs) because it holds everyday speech type of language, and a Reddit dataset as an intermediary case. Through machine learning techniques, we thus explore whether software engineering short messages exhibit notable differences compared to regular messages. For this, we utilized the cross-domain knowledge transfer approach and RoBERTa sentiment analysis technique to prove the existence of efficient models in addressing DSSMs challenges across multiple domains. Our study reveals that DSSMs are semantically different from DISMs due to F1 score differences generated by the models. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

Driving automation systems, including autonomous driving and advanced driver assistance, are an important safety-critical domain. Such systems often incorporate perception systems that use machine learning to analyze the vehicle environment. We explore new or differing topics and challenges experienced by practitioners in this domain, which relate to requirements engineering (RE), quality, and systems and software engineering. We have conducted a semi-structured interview study with 19 participants across five companies and performed thematic analysis of the transcriptions. Practitioners have difficulty specifying upfront requirements and often rely on scenarios and operational design domains (ODDs) as RE artifacts. RE challenges relate to ODD detection and ODD exit detection, realistic scenarios, edge case specification, breaking down requirements, traceability, creating specifications for data and annotations, and quantifying quality requirements. Practitioners consider performance, reliability, robustness, user comfort, and—most importantly—safety as important quality attributes. Quality is assessed using statistical analysis of key metrics, and quality assurance is complicated by the addition of ML, simulation realism, and evolving standards. Systems are developed using a mix of methods, but these methods may not be sufficient for the needs of ML. Data quality methods must be a part of development methods. ML also requires a data-intensive verification and validation process, introducing data, analysis, and simulation challenges. Our findings contribute to understanding RE, safety engineering, and development methodologies for perception systems. This understanding and the collected challenges can drive future research for driving automation and other ML systems. [ABSTRACT FROM AUTHOR]

Rapid societal development and increased engineering construction have exacerbated the disturbance of the geological environment. The impact of extreme climatic factors has grown, leading to a surge in geological disasters, with landslides emerging as particularly significant. Consequently, fundamental research in geological disaster detection or monitoring necessitates an in-depth study of the physical phenomena accompanying landslides' development, evolution, and occurrence. Exploring the signal characteristics associated with landslides is crucial to indirectly understanding their development and change processes—a scientific question deserving thorough exploration. Despite this research's importance, there is a notable gap in the investigation of the key design and specific implementation of electromagnetic instruments tailored for landslide detection. This gap is particularly pronounced in designing and implementing data acquisition software for electromagnetic instruments. This interdisciplinary research draws on theoretical frameworks from embedded computer science, software engineering, digital signal processing technology, geophysics, and engineering geology. It focuses on developing specialized data acquisition application software for landslide detection or monitoring, contributing to the scientific understanding of landslide development and providing independent intellectual property in the electromagnetic wave signal detection field. [ABSTRACT FROM AUTHOR]

One major challenge of today's product development is to master the constantly increasing product complexity driven by the interactions between different disciplines, like mechanical, electrical and software engineering. An approach to master this complexity is function-oriented model-based systems engineering (MBSE). In order to guide the developer through the process of transferring requirements into a final product design, MBSE methods are essential. However, especially in mechanics, function-oriented product development is challenging, as functionality is largely determined by the physical effects that occur in the contacts of physical components. Currently, function-oriented MBSE methods enable either the modeling of contacts or of structures as part of physical components. To create seamless function-oriented mechanical system architectures, a holistic method for modeling contacts, structures and their dependencies is needed. Therefore, this paper presents an extension of the motego method to model structures, by which the seamless parametric modeling of function-oriented mechanical system architectures from requirements to the physical product is enabled. [ABSTRACT FROM AUTHOR]

Over the past few years, the software engineering (SE) community has widely employed deep learning (DL) techniques in many source code processing tasks. Similar to other domains like computer vision and natural language processing (NLP), the state‐of‐the‐art DL techniques for source code processing can still suffer from adversarial vulnerability, where minor code perturbations can mislead a DL model's inference. Efficiently detecting such vulnerability to expose the risks at an early stage is an essential step and of great importance for further enhancement. This paper proposes a novel black‐box effective and high‐quality adversarial attack method, namely CodeBERT‐Attack (CBA), based on the powerful large pre‐trained model (i.e., CodeBERT) for DL models of source code processing. CBA locates the vulnerable positions through masking and leverages the power of CodeBERT to generate textual preserving perturbations. We turn CodeBERT against DL models and further fine‐tuned CodeBERT models for specific downstream tasks, and successfully mislead these victim models to erroneous outputs. In addition, taking the power of CodeBERT, CBA is capable of effectively generating adversarial examples that are less perceptible to programmers. Our in‐depth evaluation on two typical source code classification tasks (i.e., functionality classification and code clone detection) against the most widely adopted LSTM and the powerful fine‐tuned CodeBERT models demonstrate the advantages of our proposed technique in terms of both effectiveness and efficiency. Furthermore, our results also show (1) that pre‐training may help CodeBERT gain resilience against perturbations further, and (2) certain pre‐training tasks may be beneficial for adversarial robustness. [ABSTRACT FROM AUTHOR]

Textual documentations are frequently used in the software development process to outline features and behaviors of an application. For some people, textual descriptions may not be enough to understand what is being developed. In this scenario, multimedia resources appear as an option for software documentation, providing other ways to observe and interpret information. Objective: To identify and characterize the approaches and techniques which promote the use of multimedia in requirements engineering (RE) to support software development and maintenance. Method: A systematic mapping was conducted to find the primary studies in the literature and collect evidence for directing future research. Results: Only 27.66% of the approaches found validated their solutions through controlled experiments, showing the need to increase the use of scientific method in this area, with replications of studies that will allow to evaluating if other researchers independently will come up with the same results. In this context, the approaches/techniques identified were TRECE, MURMER, Wiki System Multimedia, Storytelling, Virtual World Environment, VisionCatcher, PRESTO4U, ReqVidA, CrowdRE, AVW, The Software Cinema Technique, Dolli Project, UTOPIA, and approaches without explicit names, which, as a rule, use multimedia resources as an additional support. Conclusions: There was a favorable consensus regarding the use of multimedia in RE. The selected studies demonstrated to be favorable to the adoption of media to persist and store the requirements of a system. Moreover, multimedia resources can improve the process of understanding the code and decrease evolution and maintenance costs. General terms are design, documentation, experimentation, human factors, multimedia, reliability, software engineering, verification, and security. [ABSTRACT FROM AUTHOR]

In the past, the focus of developing software applications was mainly on collecting, analyzing, and implementing user and business requirements. Nowadays, with the unlimited variety of software applications that serve the same purpose, it has become essential to go beyond user requirements to incorporate their emotions and values to ensure the use of those applications. However, the paucity of addressing the incorporation of human values into software engineering practices, in the literature and in the industry, makes it challenging to understand how to do it. Hence, in this study, we attempted to understand the level of adopting human values in software engineering activities, perceived usefulness, opportunities, and challenges in practice. In addition, we empirically investigated the relationship between the maturity level of the Requirements Engineering (RE) practices and the adoption of human values. To achieve those goals, we designed a survey that was distributed to software industry practitioners; 51 complete responses were received. The results showed that there is a positive relationship between the maturity level of RE and the adoption of human values. Also, most participants agreed that incorporating human values into the software design cycle is important; however, the lack of proven effective techniques and practices to capture and analyze the values are two of the main obstacles to adopting human values in practice. [ABSTRACT FROM AUTHOR]