Professional Software Engineers Research Articles

Improving The Cultivation Of Innovation Ability Of University Students Through Open Laboratories

This paper discusses how to improve the cultivation of college students' innovative ability through open laboratories. Firstly, it introduces the concept and advantages of open laboratories, then analyzes in detail the role of open laboratories in the cultivation of college students' innovation ability, and finally puts forward specific measures to improve college students' innovation ability through open laboratories. Through the implementation of these measures, we aim to improve students' innovation and practice ability, independent learning ability, practical operation ability and comprehensive quality, in order to adapt to the demand for software engineering professionals under the background of new engineering disciplines.

Students' and professionals' perceived creativity in software engineering: a comparative study

ABSTRACT Creativity is a critical skill that professional software engineers leverage to tackle difficult problems. In higher education, multiple efforts have been made to spark creative skills of engineering students. However, creativity is a vague concept that is open to interpretation. Furthermore, studies have shown that there is a gap in perception and implementation of creativity between industry and academia. To better understand the role of creativity in software engineering (SE), we interviewed 33 professionals via four focus groups and 10 SE students. Our results reveal 45 underlying topics related to creativity. When comparing the perception of students versus professionals, we discovered fundamental differences, grouped into five themes: the creative environment, application of techniques, creative collaboration, nature vs. nurture, and the perceived value of creativity. As our aim is to use these findings to install and further encourage creative problem-solving in higher education, we have included a list of implications for educational practice.

Improving Science That Uses Code

Abstract 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]

An exploratory study of software engineering in heavy-duty mobile machine automation

As the amount and complexity of software for automating heavy-duty mobile machinery is increasing, software engineering in this domain is becoming more important. To characterize the industry’s current state of software engineering and its issues to guide future research, we performed an empirical exploratory study. We interviewed 16 software engineering professionals from 13 different companies conducting business in heavy-duty mobile machines and their automation. The interviews were analyzed qualitatively, and quantification of the analysis results is presented. We first create an overview of software engineering in the heavy-duty mobile machinery industry. We then identify problem areas affecting software development and discuss some of the possible solutions found in literature. Our findings indicate that the major problem areas faced in the industry that require more research are its digital transformation, autonomous machine functional safety, low availability of workforce for developing software for robotic mobile machines and the lack of established software standards.

The Orientation and Construction Path of Software Engineering Technology Specialty at the Vocational Undergraduate Level

The development of undergraduate vocational education is the inevitable result of China's meeting the industry's demand for high-quality and multi-skilled talents. In the context of the rapid development of the domestic digital economy and the construction of information and innovation, the requirements for software technology talents are also changing. In order to meet the needs of regional industries, we should build a professional undergraduate software engineering technology major. Starting from the industrial demand, this paper first investigates and analyzes the employment demand and future trend of software technology-related enterprises, then analyzes the orientation of the specialty, and finally proposes the construction path of specialty construction and talent training, which provides support for the construction and development of software engineering technology specialty in vocational colleges.

Community smells—The sources of social debt: A systematic literature review

Social debt describes the accumulation of unforeseen project costs (or potential costs) from sub-optimal software development processes. Community smells are sociotechnical anti-patterns and one source of social debt. Because community smells impact software teams, development processes, outcomes, and organizations, we to understand their impact on software engineering. To provide an overview of community smells in social debt, based on published literature, and describe future research. We conducted a systematic literature review (SLR) to identify properties, understand origins and evolution, and describe the emergence of community smells. This SLR explains the impact of community smells on teamwork and team performance. We include 25 studies. Social debt describes the impacts of poor socio-technical decisions on work environments, people, software products, and society. For each of the 30 community smells identified as sources of social debt, we provide a detailed description, management approaches, organizational strategies, and mitigation effectiveness. We identify five groups of management approaches: organizational strategies, frameworks, models, tools, and guidelines. We describe 11 common properties of community smells. We develop the Community Smell Stages Framework to concisely describe the origin and evolution of community smells. We then describe the causes and effects for each community smell. We identify and describe 8 types of causes and 11 types of effects related to the community smells. Finally, we provide 8 comprehensive Sankey diagrams that offer insights into threats the community smells pose to teamwork factors and team performance. Community smells explain the influence work conditions have on software developers. The literature is scarce and focuses on a small number of community smells. Thus, the community smells still need more research. This review helps by organizing the state of the art about community smells. Our contributions provide motivations for future research and provide educational material for software engineering professionals.

Exploration of Teaching Mode of Software Project Management Based on Engineering Education Professional Certification

Software Project Management is a core professional course in software engineering major. Combined with engineering education professional certification, we deeply analyze some problems existing in the reform of software project management course teaching mode, and further put forward the teaching improvement strategies. A new teaching model of software project management course is explored based on the analysis of achievement of course teaching objectives, the establishment of all-channel teaching system, and the refinement of assessment and evaluation granularity. The further deepening of the teaching reform of this course can promote the achievement of the training goals of software engineering professionals, and at the same time guarantee the implementation effect of engineering education professional certification.

Trace visualization within the Software City metaphor: Controlled experiments on program comprehension

Especially with the rise of microservice architectures, software is hard to understand when just the static dependencies are known. The actual call paths and the dynamic behavior of the application are hidden behind network communication. To comprehend what is going on in the software the vast amount of runtime data (traces) needs to be reduced and visualized. This work explores more effective visualizations to support program comprehension based on runtime data. The pure DynaCity visualization supports understanding normal behavior, while DynaCity rc supports the comprehension of faulty behavior. DynaCity uses the city metaphor for visualization. Its novel trace visualization displays dynamic dependencies as arcs atop the city. To reduce the number of traces, DynaCity aggregates all requests between the same two components into one arc whose brightness reflects both the number and the total duration of the requests. DynaCity also encodes dynamic trace data in a heatmap that it uses to light up the building: the brighter a building is, the more active it is, i.e., the more and the longer the requests are that it receives and/or spawns. An additional color scheme reflects any error/status codes among the aggregated traces. In a controlled experiment, we compare our approach with a traditional trace visualization built into the same Software City but showing all dependencies (without aggregation) as individual arcs and also disabling the heatmap. We also report on a second study that evaluates if an error-based coloring of only the arcs is sufficient or if the buildings should also be colored. We call this extension DynaCity rc as it is meant to support r oot c ause analyses. The source code and the raw data of the quantitative evaluations are available from https://github.com/qaware/dynacity . We show quantitatively that a group of professional software developers who participated in a controlled experiment solve typical software comprehension tasks more correctly (11.7%) and also saved 5.83% of the total allotted time with the help of DynaCity and that they prefer it over the more traditional dynamic trace visualization. The color scheme based on HTTP error codes in DynaCity rc supports developers when performing root cause analyses, as the median of them stated that the visualization helped them much in solving the tasks. The evaluation also shows that subjects using DynaCity rc with colored arcs and buildings find the responsible component 26.2% and the underlying root cause 33.3% more correctly than the group with just colored arcs. They also ranked it 40% more helpful to color both. The DynaCity visualization helps professional software engineers to understand the dynamic behavior of a software system better and faster. The color encoding of error codes in DynaCity rc also helps them with root cause analyses. • DynaCity helps developers in software comprehension. • View is less cluttered by aggregating traces into representative arcs and by mapping activities to the brightness of both arcs and buildings. • Extension DynaCity rc supports developers in root cause analyses. • Better to map error codes to the colors of arcs and also buildings. • Controlled experiments with up to 30 professionals justify claims quantitatively.

The Princeton University Research Software Engineering Group Model: Operational and Organizational Approaches

The Princeton Research Software Engineering Group has grown rapidly since its inception in late 2016. The group is housed in the central Research Computing Department, comprised of professional Research Software Engineers (RSEs), and works directly with researchers to create high quality research software. As the group has matured, so has the need for formalizing operational details and procedures. The RSE group uses an RSE partnership model, where Research Software Engineers work long-term with a designated academic department, institute, center, consortium, or individual principal investigator (PI). This article describes the operation of the central Princeton RSE group including funding, partner & project selection, and best practices for defining expectations for a successful partnership with researchers.

Perceptions of the human and social factors that influence the productivity of software development teams in Colombia: A statistical analysis

This research aims to know if software engineering professionals consider that social and human factors (SHF) influence the productivity of a work team. A survey-based study was conducted among 112 members of software development teams. Empirical results show professionals agree with the SHF in the context of software development influence in the productivity of work teams. It was identified that the 13 SHFs have a weak or moderate correlation with each other. Additionally, the results of the exploratory factorial analysis suggest categorizing the factors into those associated with the individual, those associated with team interaction, and those related to capabilities and experience. This categorization reduced the number of items in the original questionnaire while preserving the variability explained in the latent variables, which will require a shorter response time. Our results broaden the understanding of the SHFs that influence software development team productivity and open up new research opportunities. Measuring the perception of these factors can be used to identify which SHFs should be prioritized in a strategy to improve productivity. In addition, this knowledge can help software organizations appropriately manage their development teams and propose innovative work approaches that have a positive impact on the success of their projects.

Generating customized low-code development platforms for digital twins

A digital twin improves our use of a cyber–physical system and understanding of its emerging behavior. To this effect, a digital twin is to be developed and configured and potentially also operated by domain experts, who rarely have a professional software engineering background and for whom easy access and support, e.g., in form of low-code platforms are missing. In this paper, we report on an integrated method for the model-driven engineering of low-code development platforms for digital twins that enables domain experts to create and operate digital twins for cyber–physical systems using the most appropriate modeling languages. The foundation of this method is (1) a code generation infrastructure for information systems combined with (2) an extensible base architecture for self-adaptive digital twins and (3) reusable language components for their configuration. Using this method, software engineers first configure the information system with the required modeling languages to generate the low-code development platform for digital twins before domain experts leverage the generated platform to create digital twins. This two-step method facilitates creating tailored low-code development platforms as well as creating and operating customized digital twins for a variety of applications.

An instance-based-learning simulation model to predict knowledge assets evolution involved in potential digital transformation projects

ABSTRACT Software engineering professionals must consider the appropriate technological solutions to meet their client’s needs and the organisational impact. The decision to implement a solution is not explicitly based on how it empowers the knowledge assets. Organisational knowledge assets are the foundation of the knowledge economy and a key element in evaluating the health of an organisation. This paper provides software engineers with a simulation model which illustrates the decision-making process for the implementation of technological solutions based on an evaluation of their client’s knowledge assets and how such assets impact and are impacted by the deployment of a solution. We use an agent-based approach and implement an instance-based learning model (a cognitive approach) to represent scenarios for experience-based decisions. 11 case studies were used to train the prediction engine and validate the usefulness of the model in generating scenarios and nurturing decision-making and user experiences.

Guidelines for collaborative development of sustainable data treatment software

Software development for data reduction and analysis at large research facilities is increasingly professionalized, and internationally coordinated. To foster software quality and sustainability, and to facilitate collaboration, representatives from software groups of European neutron and muon facilities have agreed on a set of guidelines for development practices, infrastructure, and functional and non-functional product properties. These guidelines have been derived from actual practices in software projects from the EU funded consortium ‘Science and Innovation with Neutrons in Europe in 2020’ (SINE2020), and have been enriched through extensive literature review. Besides guiding the work of the professional software engineers in our computing groups, we hope to influence scientists who are willing to contribute their own data treatment software to our community. Moreover, this work may also provide inspiration to scientific software development beyond the neutron and muon field.

Strategies to Evolve ExM Notations Extracted from a Survey with Software Engineering Professionals Perspective

Contemporary complex systems often exhibit dynamic structures and behaviors, several components/systems involved, and multiple interoperability links. Those systems have been exposed to fragilities of traditional software specification languages (e.g. UML and SySML), since such languages were designed to document single (not multiple interoperating) systems. Those limitations can potentially further compromise the quality of the final software product. In this context, Executable Models (ExM) technology, such as simulation models, models@runtime and executable UML, satisfy the aforementioned requirements by supporting engineers with visualization of the system structures (still at design-time) and the ability to exercise their behaviors and interactions. In our prior study, we presented the results of an exploratory study on the perceptions of those professionals (from both industry and academia) regarding the use of ExM to solve problems in their current practice. We exposed 58 professionals (researchers and practitioners) to situations to solve problems using a specific type of ExM (DEVS simulation models), based on survey research. Responses were quantitatively and qualitatively analyzed. In this article, we extended the obtained results by analyzing and compiling a list of strategies to improve ExM notations to better address the needs of software engineering professionals. Later, we assessed those strategies with software engineering researchers to confirm the importance of the proposed strategies. Results revealed that executable languages still require advances to bring them even closer to the current software engineering practice and towards a more significant adoption in the future. The proposed strategies focus on improvements on the robustness of the ExM notations, visual representation of the models, the usability of the models, and user support.

International Comparative Studies on the Software Testing Profession

This work attempts to fill a gap by exploring the human dimension in particular, by trying to understand the motivation of software professionals for taking up and sustaining their careers as software testers. Towards that goal, four surveys were conducted in four countries - India, Canada, Cuba, and China - to try to understand how professional software engineers perceive and value work-related factors that could influence their motivation to start or move into software testing careers. From our sample of 220 software professionals, we observed that very few were keen to take up testing careers. Some aspects of software testing, such as the potential for learning opportunities and the importance of the job, appear to be common motivators across the four countries, whereas the treatment of testers as second-class citizens and the complexity of the job appeared to be the most prominent de-motivators.

Enhancing Technical Q&A Forums with CiteHistory

Software engineers often use Q&A forums like Stack Overflow and MSDN to ask and answer technical questions. Through a survey study and web browser log analysis, we find that both askers and answerers of technical forum questions typically conduct extensive online research before composing their posts. The inclusion of links to these research materials is beneficial to the forum participants, though post authors do not always include such citations. Based on these findings, we developed CiteHistory, a browser plugin that simplifies the process of including relevant search queries and URLs as bibliographic supplements to forum posts, and supports information re-finding for post authors. We discuss the results of a two-week deployment of CiteHistory with professional software engineers, which demonstrated that CiteHistory increased reference inclusion in posts, and offered auxiliary benefits as a personal research tracker.

Empirically Evaluating the Effect of the Physics of Notations on Model Construction

In 2009, Moody introduced nine principles for evaluating, improving and designing cognitively effective notations called the “Physics of Notations” [49] motivating many research works ever since, being cited more than 1250 times at the time of writing this paper. Many research works have adopted the nine principles of the Physics of Notations to improve existing notations or devise new notations. Modeling is a two-step process that has the goal of communicating a mental concept by a model constructor (step one) to a model reader (step two). A subset of the research works utilizing the Physics of Notations have empirically validated the cognitive effectiveness of the new notations by their readers. However, there lacks any empirical evidence that investigates the effect of using Physics of Notations-enabled notations in model construction. This is a serious matter to be investigated as naturally model construction preludes model comprehension. Poorly constructed models can at best be poorly comprehended by its readers having dire consequences in downstream development activities. This paper reports on three different experiments that use software engineering professionals as subjects. The experiments investigate the effect of using notations that adhere to the Physics if Notations principles on model construction efforts. The results do not indicate an outright advantage for model constructors who utilize Physics of Notations-enabled notations in comparison to using their original versions of these notations.

FAITH Software Life Cycle Model for Forex Expert Advisors

The emotional stress and uncertainties associated with foreign exchange (forex) trading due to the high risk of losing the investment capital has left most forex traders in a state of indecision on the best methodology to apply for achieving long term profit. The provision of lot sizes, leverages, take profits and stop losses in forex trading implies that very high profit can be made within a very short time with the same capital, but at the same time, very high losses can be incurred. On one hand, this provision often prompts a set of traders to become greedy by increasing their take profit levels, lot sizes and leverages, which in turn increases their probability of losing out. On the other hand, the provision creates doubts and induces the fear of losses in some other set of traders. Consequently, these set of conservative traders employ the use of relatively small lot sizes, low leverages and low values of take profit and high stop loss levels. This in turn often results in a devastating effect on the investment capital due to lost opportunities and resulting losses. The problem of losses in forex trading effort is compounded by the fact that many programmers and developers of forex expert advisors do not adopt a software life cycle, having learned only how to write codes to program the trading platform. Furthermore, software engineering professionals who understand the import of software development life cycles soon discover that conventional software life cycles are not capable of effectively handling the complexity of the forex market. This paper models the human characteristics of greed, fear and doubt as manifested by traders in forex trading using selected expert advisors’ properties. It proposes Facts, Analysis, Implementation, Testing and Hope (FAITH) software life cycle model for Forex trading profitability to tackle the problem of indecision in the development of forex expert advisors. The proposed model was implemented on a live trading platform for a period of three months and compared with doubt, fear and greed approach to trading. The results showed that while a level of greed can be profitable, FAITH software life cycle produced more profitable results and can be adopted for forex trading. Keywords: Software Development Life Cycle, Expert advisors, Forex Model, Losses, Profit

Computing Academics’ Perceived Level of Awareness and Exposure to Software Engineering Code of Ethics: A Case Study of a South African University of Technology

The need for awareness on ethical computing is increasingly becoming important. As a result this challenges all stakeholders in the software engineering profession, including educators, to improve their efforts on the awareness of professional codes of ethics which provide framework for ethical reference. However, the several compromises in the software engineering practice suggest that there are some in the profession, who are not familiar with the profession’s codes of ethics and subsequently not able to practice and teach students about them. This research work investigates the extent of codes of ethics awareness by practitioners who are teaching software development courses in an academic environment. An online questionnaire with indicators for measuring awareness on software engineering code of ethics was deployed and responded to by 44 educators. Graphical, univariate and bivariate analyses were conducted on the data to determine the profile of the respondents and the extent of their level of awareness on the codes of ethics. The results indicate that majority of the lecturers (54.5 %) are not aware of software engineering codes of ethics, and those who are aware, majority of them were exposed to through self-study or personal development. Furthermore, the inclusion of codes of ethics in the learning activities is minimal as inhibited by lack of awareness and failure to apply the codes practically. This study recommends that lecturing staff as part of the professional software engineers serving as academic corps, should be placed on programmes for exposing them to professional software engineering codes of ethics. Moreover, the study calls for accreditation of software engineering courses, as it is the case with other professional engineering disciplines, to improve awareness and subsequent practical application of the codes of ethics.

Improving the success rate of applying the extract method refactoring

• A technique to improve the success rate of applying the extract method refactoring. • An empirical study about usability and refactoring tools. • A tool that proposes alternative text selection for source code refactoring. • An empirical evaluation of the proposed tool. Context: Most modern programming environments support refactorings. Although refactorings are relevant to improve the quality of software source code, they unfortunately suffer from severe usability issues. In particular, the extract method refactoring, one of the most prominent refactorings, has a failure rate of 49% when users attempt to use it. Objective: Our main objective is to improve the success rate of applying the extract method refactoring. Methods: First, to understand the cause of refactoring failure, we conducted a partial replication of Vakilian's ICSE '14 study about usability issues of refactoring using IntelliJ IDEA. Second, we designed and implemented TOAD, a tool that proposes alternative text selection for source code refactoring for the Pharo programming language. Third, we evaluated TOAD using a controlled experiment against the standard Pharo code refactoring tool. Seven professional software engineers complemented with three undergrad students participated in our experiments. Conclusion: The causes we identified of failed extract method refactoring attempts match Vakilian's work. TOAD significantly reduces the number of failed attempts to run the extract method refactoring at a lower cognitive load cost.