🏎️ Shared best-practice guidance & tools to support software engineering teams

A repository for robust and reliable software solutions. This collection focuses on enterprise-level applications, frameworks, and tools designed for scalability, security, and long-term maintenance, reflecting a commitment to professional and high-quality software engineering.

This framework provides a modular approach to software engineering quality management

The Breast Radar-based Image Quality Analysis (BRIQS) Framework was created as part of a Master's Year (MAI Biomedical Engineering) Project at Trinity College Dublin. BRIQS is a free and open-source framework for Microwave Radar-based Imaging. It builds upon the BRIGID phantom dataset and MERIT software.

Gemini Kit is a multi-agent AI development framework built on top of the Gemini CLI. It coordinates specialized agents and modular skills to automate software engineering workflows, delivering high-quality results with strong architectural consistency and optimized token usage.

Software engineering project in the third year of bachelor's studies. The project includes the development of mobile and web applications. A flutter development framework is used to develop client applications. The server side was developed on .NET Core, and a SQLite database was used as a repository. A team of 5 members and a quality tester participated in the development of applications. The development of the project lasted 13 weeks. The theme of the project is to develop a social network that will raise environmental awareness.

Recently, machine learning (ML) technology has become widespread. They are used in a variety of areas, including network protection, IoT, and self-sufficient vehicles. ML technology relies on science and computer programming. Science is used to perform calculations, develop the ability to extract from input information, and create agent models. Programming, on the other hand, is used for execution and heartfelt execution. Much of the work also explores the science of software engineering in which ML technology is manufactured, but few have analyzed its implementation, raising many concerns. The first is the product complexity of ML technology. The second is the type of execution that you can access, such as execution and reliability. The third is the type of model that can be affected by product defects. If developers can demonstrate the feasibility of implementing the ML approach, these concerns may be mitigated. Therefore, experts and experts focus on best practices in addition to designing and programming the ML framework to solve problems related to programming complexity and the nature of ML technology. Such practices are often formalized as construction and design patterns, embodying reusable answers to problems that commonly occur within certain settings of the ML framework and programming design. Machine learning (ML) technology has proven to be more prevalent. ML technology relies on mathematics and programming. Analysts and professionals focus on the best samples and strive to design and program ML frameworks that take into account the complexity and quality issues of site programming. Such design samples are often formalized as build and design patterns by entering reusable answers to common problems in a particular environment. However, accurate reports on the collection, characterization, and research of these design patterns in computer programming (SE) of the ML method still do not seem to be understood. This collects good / bad SE design patterns for ML techniques and allows developers to fully characterize such patterns. In this report, the basic results of an accurate written audit (SLR) of good / bad design patterns in ML.

Location Dinovative is looking for Back-end NodeJS Developer who is excited to work alongside a talented group of innovators to craft digital products. Being part of the conversation from the start, you will be expected to lead full lifecycle webbased projects including guiding technical scoping, design and implementation. Collaborating with UX & UI designers, researchers and other engineers (web & mobile), you will have the ability to flex your full stack chops to help shape, design and build digital products that help solve key business needs. We want people who love being involved in challenging and innovative work. 63A Nam Ky Khoi Nghia, Ben Thanh Ward, District 1 Salary Expectation Negotiable Requirements Minimum 2-3 year working experience with Nodejs Solid understanding of front-end technologies, such as JavaScript, HTML5, and CSS3 Strong experience with RESTful APIs and WebSocket APIs Experience working with database system such as mySQL, MongoDB, NoSQL Solid understanding of object-oriented programming Understanding fundamental design principles behind a scalable application and microservice architecture Experience in front-end development (ReactJS) is a big advantage Strong analytical and problem solving abilities Strong communication and client facing skills Skills Javascript Nodejs PostgreSQL Setting up servers (nginx etc.) and automating deployment process (Docker, Ansible, Chef etc.) Ideally Experience setting up servers (nginx etc.) and automating deployment process (Docker, Ansible, Chef etc.) Experience with ES6 Features, MEAN stack, other frameworks such as ExpressJS, Loopback ,... Responsibilities Design, build, and maintain efficient, reusable, and reliable Nodejs code Integration of data storage solutions Help maintain code quality, organization and automatization Constantly learn and keep abreast of emerging technologies Contribute to the software design processes including whiteboarding sessions, workshops and prototyping Critique software designs and architectures Peer review colleagues code and identifying areas for improvement Provide development task effort estimates Conduct client requirements gathering and analysis Review test plans Follow defined development best practice including commenting and documenting code, contribute to development wikis and using source control Why it would be awesome to work with us Come and work with us. You will have opportunity to learn new technologies with international standard, build products that are different/innovative and get various benefits: Working in dynamic, young, friendly, flexible invironment Laptop is provided 12+ days annual leaves, working Monday - Friday, flexible working time Attractive salary based on skills and experiences Salary review: Once a year, base on individual performance review Performance bonus: Twice a year (every 6 months), base on individual performance, profit and company policy Gift occasions: 8/3; 1/6 (for children); Mid-autumn festival; 20/10; Lunar New Year, Wedding Free fruits/ snacks on happy hours time Celebration: birthday Party time: Team building (frequent) and Xmas Healthcare, Unemployment insurance and Sick leave: based on current relevant Laws Great opportunity for career development Training new technology frequently, and become a fullstack developer Interesting engineering projects We use international standard in building system such as agile, test driven development, continuous integration and continuous deployment. Various projects with so many new technologies applied Contact Email: oanh.tang@dinovative.com Phone: 0909 617 173 Skype: tangoanh FB: https://www.facebook.com/oanhtang195

Bigben-Software A repository for robust and reliable software solutions. This collection focuses on enterprise-level applications, frameworks, and tools designed for scalability, security, and long-term maintenance, reflecting a commitment to professional and high-quality software engineering.

A prompt software framework for Claude that eliminates sycophancy and creates an intellectually honest AI partner. Uses internal monitoring indicators to control output quality, blocks hollow praise and emotional performance, and provides prompt commands for explicit mode switching. Not magic prompts—engineering.

Graduation Project: “Online Scientific Journalism Platform’’ The software project is a web-based application was that developed by using the Django framework and Python programming language. The online scientific journalism platform provides writers to publish their articles while readers to read free articles published, buy priced articles published, or subscribe to the platform. Articles published on the platform can contain multimedia data, the article status can take online, editorial, archived, free and priced, and the articles are related to all areas of science and engineering. The platform is multilingual (Arabic and English). To guarantee the quality of the articles published the submitted articles will undergo rigorous review and editing processes through an editorial board and reviewers. Our team for the graduation project consists of Three software engineers with the advisor professor from the software engineering department. Our team applied software engineering knowledge and skills through software development life cycle processes.

This paper describes a simple approach in JUnit framework used for unit testing in an application. However, unit testing is a practical approach to increasing the correctness and quality of software. Along with that, writing unit test code is labor-intensive, hence it is often not done as an integral part of programming. It uses a formal specification language's runtime assertion checker to decide whether methods are working correctly, thus automating the writing of unit test oracles. We implemented a simple Login Web page with user id and password. A unit test is the smallest testable part of an application. In basic learning of unit testing, JUnit framework with interactive GUI techniques as suitable tools in supporting students learning in higher learning institutes. The result shows by using JUnit framework in unit testing learning can improve student interest and understanding in software engineering courses.

I am a proactive person who believes that a strong dedication towards quality work and professional attitude brings success to the work industry. Critical thinking, passion, and dedication are my framework for success. My ambition is to provide fresh perspectives and trying new techniques that will help the company evolve. On my career path I want to grow a career as Software Developer and Data Scientist where I would implement new skills while providing high ethical standards to the field of work. I thrive in environments where I am able to make a direct impact utilizing analytical thinking skills and complex problem-solving to find solutions and achieve results. I pride myself in possessing and having demonstrated good interpersonal skills including; communication, collaboration, planning. Throughout my past experience in Data engineering, I have gained working proficiency in the following tools: SQL, R, Power BI, Machine learning, Tableau, SAS, SPSS, Excel and Python I now have the knowledge of; Analyzing local, national, and global trends that impact both the organization and the industry using statistical tools/Machine learning in a complex datasets to prepare reports for the management that could be helpful for the diagnosis and prediction to assess business performance over periods of time. Throughout my academic year I manage to work with C# (build web apps that can be supported with Windows servers) and MySQL to build web development. Lastly I am an honest person that's eager to learn.

How much is it worth to catch more bugs early in your product release process? Depending on where you are in your release process, you might be writing unit or systems tests. But, you need to run end-to-end tests to prove behavior, and quality engineers require a high degree of skill to write end-to-end tests successfully. What would you say if a single validation engine could help you ensure data integrity, functional integrity, and graphical integrity in your web and mobile applications? And, as a result, catch more bugs earlier in your release process? Catch Bugs or Die Let’s start with the dirty truth: all software has bugs. Your desire to create bug-free code conflicts with the reality that you often lack the tools to uncover all the bugs until someone finds them way late in the product delivery process. Like, say, the customer. surprised 1184889 1280 With all the potential failure modes you design for – and then test against – you begin to realize that not all failure modes are created equal. You might even have your own triage list: Security & penetration Data integrity and consistency Functional integrity and consistency So, where does graphical integrity and consistency fit on your list? For many of your peers, graphical integrity might not even show up on their list. They might consider graphical integrity as managing cosmetic issues. Not a big deal. Lots of us don’t have reliable tools to validate graphical integrity. We rely on our initial unit tests, systems tests, and end-to-end tests to uncover graphical issues – and we think that they’re solved once they’re caught. Realistically, though, any application evolution process introduces changes that can introduce bugs – including graphical bugs. But, who has an automation system to do visual validation with a high degree of accuracy? Tradeoffs In End-to-End Testing Your web and mobile apps behave at several levels. The level that matters to your users, though, happens at the user interface on the browser or the device. Your server code, database code, and UI code turns into this representation of visual elements with some kind of visual cursor that moves across a plane (or keyboard equivalent) to settle on different elements. The end-to-end test exercises all the levels of your code, and you can use it to validate the integrity of your code. code 2434271 1280 So, why don’t people think to run more of these end-to-end tests? You know the answers. First, end-to-end tests run more slowly. Page rendering takes time – your test code needs to manipulate the browser or your mobile app, execute an HTTP request, receive an HTTP response, and render the received HTML, CSS, and JavaScript. Even if you run tests in parallel, they’re slower than unit or system tests. Second, it takes a lot of effort to write good end-to-end tests. Your tests must exercise the application properly. You develop data and logic pre-conditions for each test so it can be run independently of others. And, you build test automation. Third, you need two kinds of automation. You need a controller that allows you to control your app by entering data and clicking buttons in the user interface. And, most importantly, you need a validation engine that can capture your output conditions and match those with the ones a user would expect. You can choose among many controllers for browsers or mobile devices. Still, why do your peers still write code that effectively spot-checks the DOM? Why not use a visual validation engine that can catch more bugs? Visual AI For Code Integrity You have peers who continue to rely on coded assertions to spot-check the DOM. Then you have the 288 of your peers who did something different: they participated in the Applitools Visual AI Rockstar Hackathon. And they got to experience first-hand the value of Visual AI for building and maintaining end-to-end tests. As I wrote previously, we gave participants five different test cases, asked them to write conventional tests for those cases, and then to write test cases using Applitools Visual AI. For each submission, we checked the conditions each test writer covered, as well as the failing output behaviors each test-writer caught. As a refresher, we chose five cases that one might encounter in any application: Comparing two web pages Data-driven verification of a function Sorting a table Testing a bar chart Handling dynamic web content For these test cases, we discovered that the typical engineer writing conventional tests to spot-check the DOM spent the bulk of their time writing assertion code. Unfortunately, the typical spot-check assertions missed failure modes. The typical submission got about 65% coverage. Alternatively, the engineers who wrote the tests that provided the highest coverage spent about 50% more time writing tests. However, when using Visual AI for visual validation, two good things happened. First, everyone spent way less time writing test code. The typical engineer went from 7 hours of coding tests and assertions to about 1.2 hours of coding tests and Visual AI. Second, the average test coverage jumped from 65% to 95%. So, simultaneously, engineers took less time and got more coverage. Visual AI Helps You Catch More Bugs When you find more bugs, more quickly, with less effort, that’s significant to your quality engineering efforts. You’re able to validate data, functional, and graphical by focusing on the end-to-end test cases you run. You spend less time thinking about and maintaining all the assertion code checking the result of each test case. Using Visual AI makes you more effective? How much more effective? Based on the data we reviewed – you catch 45% of your bugs earlier in your release process (and, importantly, before they reach customers). VisualAI Impact 45MoreBugs With Title We have previously written about some of the other benefits that engineers get when using Visual AI, including: 5.8x Faster Test Creation – Authoring new tests is vital especially for new features during a release cycle. Less time authoring means more time managing quality. Read more. 5.9x More Test Code Efficient – Like your team’s feature code, test code efficiency means you write less code, yet provide far more coverage. Sounds impossible, right? It’s not. Read More. 3.8x Improvement In Test Stability – Code-based frameworks rely on brittle locators and labels that break routinely. This maintenance kills your release velocity and reduces coverage. What you need is self-maintaining and self-healing code that eliminates most of the maintenance. It sounds amazing and it is! Read More. By comparing and contrasting the top participants – the prize winners – with the average engineer who participated in the Hackathon, we learned how Visual AI helped the average engineer greatly – and the top engineers become much more efficient. VisualAI Impact VisualAI Impact 45MoreBugs GranPrize Compare With Title The bottom line with Visual AI — you will catch more bugs earlier than you do today. VisualAI Impact 45MoreBugs GranPrize Quote More About The Applitools Visual AI Rockstar Hackathon Applitools ran the Applitools Visual AI Rockstar Hackathon in November 2019. Any engineer could participate, and 3,000 did so from around the world. 288 people actually completed the Hackathon and submitted code. Their submissions became the basis for this article. You can read the full report we wrote: The Impact of Visual AI on Test Automation. In creating the report, we looked at three groups of quality engineers including: All 288 Submitters – This includes any quality engineer that successfully completed the hackathon project. While over 3,000 quality engineers signed-up to participate, this group of 288 people is the foundation for the report and amounted to 3,168 hours, or 80 weeks, or 1.5 years of quality engineering data. Top 100 Winners – To gather the data and engage the community, we created the Visual AI Rockstar Hackathon. The top 100 quality engineers who secured the highest point total for their ability to provide test coverage on all use cases and successfully catch potential bugs won over $40,000 in prizes. Grand Prize Winners – This group of 10 quality engineers scored the highest representing the gold standard of test automation effort. By comparing and contrasting the time, effort, and effectiveness of these groups, we were able to draw some interesting conclusions about the value of Visual AI in speeding test-writing, increasing test coverage, increasing test code stability, and reducing test maintenance costs. What’s Next? You now know five of the core benefits we calculate from engineers who use Visual AI. Spend less time writing tests Write fewer lines of test code Maintain fewer lines of test code Your test code remains much more stable Catch more bugs So, what’s stopping you from trying out Visual AI for your application delivery process? Applitools lets you set up a free Applitools account and start using Visual AI on your own. You can download the white paper and read about how Visual AI improved the efficiency of your peers. And, you can check out the Applitools tutorials to see how Applitools might help your preferred test framework and work with your favorite test programming language.

[Training] The repository for the Quality Software Engineering Challenge - Mission 006 - Mission 006 - The Queue Framework. See https://github.com/krist00fer/qsec/

Selenium + Java + Cucumber UI Automation Framework 15+ test cases with Page Object Model Allure reports, Excel data-driven, Database/Redis integration

An overview of software engineering processes. A generic process framework provides the groundwork for formal process models. Prescriptive process models include Waterfall Model and Agile Development. An intro to the elements and phases of software engineering included explores requirements engineering, design concepts, and software quality.

As a student of Software Engineering, I made a seminary about Tests and Mocking Frameworks on a Test And Quality Software Subject. In this case, the focus was on the Mockito framework.

Software Quality Engineering (Assignment: Code Coverage Analysis) | MS Word | Python (pytest) | Employed pytest framework to verify the accuracy of the Basic Calculation Function

👋 Hi there! I'm a passionate Software Automation Engineer with a strong foundation in SDET and Full Stack Quality Engineering. I specialize in creating robust test automation frameworks, ensuring high-quality software delivery, and exploring new technologies. Let's build reliable software together!

Course : Software Quality Engineering | Python(pytest) | pytest framework to ensure the correctness of the calculateGrade function. These tests are organized in a separate module called test_grade.py.

Interactive presentation on 'Vibe Coding vs Software Engineering' - A 21-slide exploration of the 4R Framework for responsible AI-assisted development, covering security, code quality, testing, and resilience patterns.

Repository showcasing QA engineering skills including test automation, manual testing, bug tracking, and test case design. Features scripts and frameworks for functional, regression, and performance testing to ensure software quality and reliability.

Software engineering methodologies are structured approaches used to plan, organize, and manage the software development process. They provide a framework for teams to develop high-quality software efficiently, within budget, and on schedule. These methodologies often define roles, responsibilities, activities, and deliverables within a project.

SoftTics is a (fictional) company specializing in software development, focused on delivering innovative, high-quality solutions. We are based on the principles of Software Engineering and work under the agile Scrum framework, which allows us to deliver products efficiently, flexibly, and in line with our customers' needs.

Software quality has become the lever of differentiation in today’s competitive marketplace. Quality at speed is the customer demand and automation is the biggest bottleneck holding the evolution of quality function. Increased levels of automation and intelligence in software engineering are the emerging trends across the IT field. As systems and software processes guide the life cycle activities and are the vehicles for building quality, it is necessary to look at the process infrastructure for the extent of process automation support provided and the digital enablement. This paper maps out the existing process infrastructure support in industry practice and proposes a roadmap for digital re-imagination of software and systems processes. Harmonizing the quality engineering themes with digital technologies, we propose a framework for building an intelligent software process infrastructure, iSPIN that can help in digital re-imagination of software and systems lifecycle processes. The framework has been implemented using digital technologies and has been piloted with one of the industry business unit for re-imagination of “proposal process”. The proposed iSPIN framework will help in unprecedented automation and quality

RefactoCNN-System is a deep learning framework for automated detection of refactoring opportunities in Java code. It combines token and AST representations, processes them with a CNN classifier, and generates interpretable refactoring suggestions to support code quality analysis, maintenance, and intelligent software engineering.

This repository contains my end-semester project for Software Quality Engineering (SQE). The project implements a complete Web UI Test Automation Framework using: Tech Stack: Java Selenium WebDriver Cucumber (BDD) JUnit / TestNG Maven Page Object Model (POM) Allure Reports Apache POI (Excel) MySQL (Database) Redis (Jedis)

Nowadays is unacceptable that software are developed without Software Testing, however many companies neglect this important discipline of Software Engineering, due to qualified resources available and your high cost during the development process. In order to show the importance of Software Test, will be held during this work the comparative analysis between the implementation of Unit Tests and Integration Tests over Web software developed as a case study. That way it will be possible to deliver related code developed on the two types of tests. For do the development the software with name KeepInShape, was used the guidelines in the book Domain Driven Design together with the frameworks Spring Boot, Spring Data and Hibernate. With the intention to also ensure the quality of Unit Testing and Integration Testing developed, was used technique of Default-Based Testing known as Mutant Analysis, making it possible to analyze qualitatively both test developed. In the same way, the SonarQube tool and the JaCoCo framework were used to ensure the quality and coverage of the code that was developed. Through this analysis it was possible to evaluate the applicability of Unit Tests and Integration Tests through the results obtained, highlighting their main advantages and disadvantages.

Orchid is a leading custom software development company. Orchid understands that for companies to grow, to differentiate, to stay relevant, it’s critical to find new ways to engage, retain and build loyalty with customers -- continually delivering better services, experiences and content Orchid strength is the ability to provide a seamless blend of creative design and user experience engineering, combined with a rigorous, productized approach to digital platform development and data analytics. Product engineering is central to the company’s heritage. Using our Orchid Connected framework, we help companies define and develop the right digital products and services faster to significantly accelerate time to market, improve customer engagement and reduce business risk. As a custom software development firm, Orchid believes digital platform development and product engineering requires a fundamentally different approach than running traditional enterprise IT services or providing staff augmentation. Orchid is driven by the challenge and excitement of invention. We cultivate innovation in close collaboration with our clients and through continuous knowledge exchange, such as our global conference. The company invests in highly-collaborative and transparent relationships with its clients, so clients can focus on their business strategies and Orchid helps them build the technology platforms that support those strategies. Our clients tell us that they are able to generate significant business value from our innovative, high quality, on-time and on-budget work, as illustrated by the long-term relationships Orchid develops with its clients.