The Presence and the State-of-Practice of Software Architects in the
Brazilian Industry - A Survey
- URL: http://arxiv.org/abs/2403.00955v1
- Date: Fri, 1 Mar 2024 20:10:37 GMT
- Title: The Presence and the State-of-Practice of Software Architects in the
Brazilian Industry - A Survey
- Authors: Valdemar Vicente Graciano Neto, Diana Lorena Santos, Andrey
Gon\c{c}alves Fran\c{c}a, Rafael Z. Frantz, Edson de Oliveira-Jr, Ahmad
Mohsin, Mohamad Kassab
- Abstract summary: The aim of this work is to understand the characteristics of the companies regarding the presence or absence of software architects in Brazil.
The study collected data from 105 professionals distributed in 24 Brazilian states.
- Score: 1.3392307634669538
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Context: Software architecture intensely impacts the software quality.
Therefore, the professional assigned to carry out the design, maintenance and
evolution of architectures needs to have certain knowledge and skills in order
not to compromise the resulting application. Objective: The aim of this work is
to understand the characteristics of the companies regarding the presence or
absence of software architects in Brazil. Method: This work uses the Survey
research as a means to collect evidence from professionals with the software
architect profile, besides descriptive statistics and thematic analysis to
analyze the results. Results: The study collected data from 105 professionals
distributed in 24 Brazilian states. Results reveal that (i) not all companies
have a software architect, (ii) in some cases, other professionals perform the
activities of a software architect and (iii) there are companies that, even
having a software architecture professional, have other roles also performing
the duties of such a professional. Conclusions: Professionals hired as software
architects have higher salaries than those hired in other roles that carry out
such activity, although many of those other professionals still have duties
that are typical of software architects.
Related papers
- DSBench: How Far Are Data Science Agents to Becoming Data Science Experts? [58.330879414174476]
We introduce DSBench, a benchmark designed to evaluate data science agents with realistic tasks.
This benchmark includes 466 data analysis tasks and 74 data modeling tasks, sourced from Eloquence and Kaggle competitions.
Our evaluation of state-of-the-art LLMs, LVLMs, and agents shows that they struggle with most tasks, with the best agent solving only 34.12% of data analysis tasks and achieving a 34.74% Relative Performance Gap (RPG)
arXiv Detail & Related papers (2024-09-12T02:08:00Z) - Please do not go: understanding turnover of software engineers from different perspectives [5.959478613390186]
We identify 19 different reasons for software engineers' turnover and 18 more efficient strategies used in the software development industry to reduce it.
Our findings provide several implications for industry and academia, which can drive future research.
arXiv Detail & Related papers (2024-06-29T01:31:06Z) - Comparison of Static Analysis Architecture Recovery Tools for
Microservice Applications [43.358953895199264]
We will identify static analysis architecture recovery tools for microservice applications via a multi-vocal literature review.
We will then execute them on a common dataset and compare the measured effectiveness in architecture recovery.
arXiv Detail & Related papers (2024-03-11T17:26:51Z) - Charting a Path to Efficient Onboarding: The Role of Software
Visualization [49.1574468325115]
The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools.
This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
arXiv Detail & Related papers (2024-01-17T21:30:45Z) - What rationales drive architectural decisions? An empirical inquiry [0.7499722271664147]
There is a rationale behind every architectural decision that motivates an architect to choose one architectural solution out of a set of options.
This study aims to identify which categories of rationale most frequently impact architectural decisions and investigates why these are important to practitioners.
arXiv Detail & Related papers (2023-09-25T14:18:51Z) - Architecture Knowledge Representation and Communication Industry Survey [0.0]
We aim to understand the current practice in architecture knowledge, and to explore where sustainability can be applied to address sustainability in software architecture in the future.
We used a survey, which utilized a questionnaire containing 34 questions and collected responses from 45 architects working at a prominent bank in the Netherlands.
arXiv Detail & Related papers (2023-09-20T18:17:16Z) - Software Architecture in Practice: Challenges and Opportunities [19.919430428287917]
We identified challenges that practitioners face in software architecture practice during software development and maintenance.
Our study uncovers that most of these challenges center around management, documentation, tooling and process.
arXiv Detail & Related papers (2023-08-19T10:58:47Z) - Machine Learning-Enabled Software and System Architecture Frameworks [48.87872564630711]
The stakeholders with data science and Machine Learning related concerns, such as data scientists and data engineers, are yet to be included in existing architecture frameworks.
We surveyed 61 subject matter experts from over 25 organizations in 10 countries.
arXiv Detail & Related papers (2023-08-09T21:54:34Z) - A Reference Software Architecture for Social Robots [64.86618385090416]
We propose a series of principles that social robots may benefit from.
These principles lay also the foundations for the design of a reference software architecture for Social Robots.
arXiv Detail & Related papers (2020-07-09T17:03:21Z) - Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems.
No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages.
This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
arXiv Detail & Related papers (2020-05-27T11:56:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.