DAnTE: a taxonomy for the automation degree of software engineering tasks

Software engineering researchers and practitioners have pursued manners to reduce the amount of time and effort required to develop code and increase productivity since the emergence of the discipline. Generative language models are just another step in this journey, but it will probably not be the last one. In this chapter, we propose DAnTE, a Degree of Automation Taxonomy for software Engineering, describing several levels of automation based on the idiosyncrasies of the field. Based on the taxonomy, we evaluated several tools used in the past and in the present for software engineering practices. Then, we give particular attention to AI-based tools, including generative language models, discussing how they are located within the proposed taxonomy, and reasoning about possible limitations they currently have. Based on this analysis, we discuss what novel tools could emerge in the middle and long term.Comment: 15 pages, 1 figur

Case Survey Studies in Software Engineering Research

Background: Given the social aspects of Software Engineering (SE), in the last twenty years, researchers from the field started using research methods common in social sciences such as case study, ethnography, and grounded theory. More recently, case survey, another imported research method, has seen its increasing use in SE studies. It is based on existing case studies reported in the literature and intends to harness the generalizability of survey and the depth of case study. However, little is known on how case survey has been applied in SE research, let alone guidelines on how to employ it properly. Aims: This article aims to provide a better understanding of how case survey has been applied in Software Engineering research. Method: To address this knowledge gap, we performed a systematic mapping study and analyzed 12 Software Engineering studies that used the case survey method. Results: Our findings show that these studies presented a heterogeneous understanding of the approach ranging from secondary studies to primary inquiries focused on a large number of instances of a research phenomenon. They have not applied the case survey method consistently as defined in the seminal methodological papers. Conclusions: We conclude that a set of clearly defined guidelines are needed on how to use case survey in SE research, to ensure the quality of the studies employing this approach and to provide a set of clearly defined criteria to evaluate such work.Comment: Accepted for presentation at ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) (ESEM '20

Business Model Canvas Should Pay More Attention to the Software Startup Team

Business Model Canvas (BMC) is a tool widely used to describe startup business models. Despite the various business aspects described, BMC pays a little emphasis on team-related factors. The importance of team-related factors in software development has been acknowledged widely in literature. While not as extensively studied, the importance of teams in software startups is also known in both literature and among practitioners. In this paper, we propose potential changes to BMC to have the tool better reflect the importance of the team, especially in a software startup environment. Based on a literature review, we identify various components related to the team, which we then further support with empirical data. We do so by means of a qualitative case study of five startups

Generative Artificial Intelligence for Software Engineering -- A Research Agenda

Generative Artificial Intelligence (GenAI) tools have become increasingly prevalent in software development, offering assistance to various managerial and technical project activities. Notable examples of these tools include OpenAIs ChatGPT, GitHub Copilot, and Amazon CodeWhisperer. Although many recent publications have explored and evaluated the application of GenAI, a comprehensive understanding of the current development, applications, limitations, and open challenges remains unclear to many. Particularly, we do not have an overall picture of the current state of GenAI technology in practical software engineering usage scenarios. We conducted a literature review and focus groups for a duration of five months to develop a research agenda on GenAI for Software Engineering. We identified 78 open Research Questions (RQs) in 11 areas of Software Engineering. Our results show that it is possible to explore the adoption of GenAI in partial automation and support decision-making in all software development activities. While the current literature is skewed toward software implementation, quality assurance and software maintenance, other areas, such as requirements engineering, software design, and software engineering education, would need further research attention. Common considerations when implementing GenAI include industry-level assessment, dependability and accuracy, data accessibility, transparency, and sustainability aspects associated with the technology. GenAI is bringing significant changes to the field of software engineering. Nevertheless, the state of research on the topic still remains immature. We believe that this research agenda holds significance and practical value for informing both researchers and practitioners about current applications and guiding future research

Empirical Standards for Software Engineering Research

Empirical Standards are natural-language models of a scientific community's expectations for a specific kind of study (e.g. a questionnaire survey). The ACM SIGSOFT Paper and Peer Review Quality Initiative generated empirical standards for research methods commonly used in software engineering. These living documents, which should be continuously revised to reflect evolving consensus around research best practices, will improve research quality and make peer review more effective, reliable, transparent and fair.Comment: For the complete standards, supplements and other resources, see https://github.com/acmsigsoft/EmpiricalStandard

Engenharia de requisitos em startups de software: uma investigação qualitativa

Software startups face a very demanding market: they must deliver high innovative solutions in the shortest possible period of time. Resources are limited and time to reach market is short. Then, it is extremely important to gather the right requirements and that they are precise. Nevertheless, software requirements are usually not clear and startups struggle to identify what they should build. This context affects how requirements engineering activities are performed in these organizations. This work seeks to characterize the state-of-practice of requirements engineering in software startups. Using an iterative approach, seventeen interviews were conducted during three stages with founders and/or managers of different Brazilian software startups operating in different market sectors and with different maturity levels. Data was analyzed using grounded theory techniques such open and axial coding through continuous comparison. As a result, a conceptual model of requirements engineering state-of-practice in software startups was developed consisting of its context influences (founders, software development manager, developers, business model, market and ecosystem) and activities description (product team; elicitation; analysis, validation and prioritization; product validation and documentation). Software development and startup development techniques are also presented and their use in the startup context is analyzed. Finally, using a bad smell analogy borrowed from software development literature, some bad practices and behaviors identified in software startups are presented and solutions to avoid them proposed.Startups de software enfrentam um mercado muito exigente: elas devem entregar soluções altamente inovativas no menor período de tempo possível. Recursos são limitados e tempo para alcançar o mercado é pequeno. Então, é extremamente importante coletar os requisitos certos e que eles sejam precisos. Entretanto, os requisitos de software geralmente não são claros e as startups fazem um grande esforço para identificar quais serão implementados. Esse contexto afeta como as atividades de engenharia de requisitos são executadas nessas organizações. Este trabalho procura compreender o estado-da-prática da engenharia de requisitos em startups de software. Usando uma abordagem iterativa, dezessete entrevistas foram realizados em três diferentes estágios com fundadores e/ou gestores de diferentes startups de software brasileiras operando em diferentes setores e com diferentes estágios de maturidade. Os dados foram analisados usando técnicas de teoria fundamentada como codificação aberta e axial através da comparação contínua. Como resultado, um modelo conceitual do estado-da-prática da engenharia de requisitos em startups de software foi desenvolvido consistindo da suas influências do contexto (fundadores, gerente de desenvolvimento de software, desenvolvedores, modelo de negócio, mercado e ecossistema) e descrição das atividades (time de produto; levantamento; análise, validação e priorização; e documentação). Técnicas oriundas de metodologias de desenvolvimento de software e desenvolvimento de startups também são apresentadas e seu uso em no contexto de startups é analisado. Finalmente, a partir de uma analogia de maus cheiros presente na literatura de desenvolvimento de software, algumas más práticas e maus comportamentos identificados em startups de software são apresentados e algumas sugestões de solução são propostas

Hypotheses engineering : first essential steps of experiment-driven software development

Recent studies have proposed the use of experiments to guide software development in order to build features that the user really wants. Some authors argue that this approach represents a new way to develop software that is different from the traditional requirement-driven one. In this position paper, we propose the discipline of Hypotheses Engineering in comparison to Requirements Engineering, highlighting the importance of proper handling hypotheses that guide experiments. We derive a set of practices within this discipline and present how the literature has tackled them up to now. Finally, we propose a set of research questions that could guide future work towards helping practitioners.peerReviewe

GEDAE-LaB: A Free Software to Calculate the Energy System Contributions during Exercise.

PURPOSE:The aim of the current study is to describe the functionality of free software developed for energy system contributions and energy expenditure calculation during exercise, namely GEDAE-LaB. METHODS:Eleven participants performed the following tests: 1) a maximal cycling incremental test to measure the ventilatory threshold and maximal oxygen uptake (V̇O2max); 2) a cycling workload constant test at moderate domain (90% ventilatory threshold); 3) a cycling workload constant test at severe domain (110% V̇O2max). Oxygen uptake and plasma lactate were measured during the tests. The contributions of the aerobic (AMET), anaerobic lactic (LAMET), and anaerobic alactic (ALMET) systems were calculated based on the oxygen uptake during exercise, the oxygen energy equivalents provided by lactate accumulation, and the fast component of excess post-exercise oxygen consumption, respectively. In order to assess the intra-investigator variation, four different investigators performed the analyses independently using GEDAE-LaB. A direct comparison with commercial software was also provided. RESULTS:All subjects completed 10 min of exercise at moderate domain, while the time to exhaustion at severe domain was 144 ± 65 s. The AMET, LAMET, and ALMET contributions during moderate domain were about 93, 2, and 5%, respectively. The AMET, LAMET, and ALMET contributions during severe domain were about 66, 21, and 13%, respectively. No statistical differences were found between the energy system contributions and energy expenditure obtained by GEDAE-LaB and commercial software for both moderate and severe domains (P > 0.05). The ICC revealed that these estimates were highly reliable among the four investigators for both moderate and severe domains (all ICC ≥ 0.94). CONCLUSION:These findings suggest that GEDAE-LaB is a free software easily comprehended by users minimally familiarized with adopted procedures for calculations of energetic profile using oxygen uptake and lactate accumulation during exercise. By providing availability of the software and its source code we hope to facilitate future related research