Being a QA professional is a challenging task and sometimes, pressured by deadlines, spontaneous errors, stress and pressure, QA engineers develop harmful habits that they inadvertently apply to the work process.

Today we will present you the top 3 bad habits for QA professionals, according to Simple Programmer.

Bad Habit #1: Assuming Strange Behavior is Right Behavior

Often when testing a new feature, QA professionals encounter a strange behavior. Sometimes it has to do with refreshing a page or navigating to a place they weren’t expecting. Or perhaps an unexpected button appears. And when QA engineers have a deadline, they miss these seemingly insignificant changes that actually have a significant impact on the final result and quality. Furthermore, the fact that these quirks didn’t impress you doesn’t mean that end users won’t notice them and may even quit using the app or product because of them.

Bad Habit #2. Getting caught up in the details

This is the opposite of bad habit #1. Sometimes QA engineers are so focused on finding every single thing that’s wrong with an application, no matter how minor, that they fall into “analysis paralysis” and halt their team’s progress.

Experts advise on focusing on actual usage scenarios. The QA engineer’s goal is to ensure that the software works effectively for users and is protected against malicious attacks. Searching for bugs for the sheer pleasure of searching is the wrong approach that hinders productivity.

Bad Habit #3. Writing automated tests

QA professionals who have become familiar with writing automated tests often begin to feel a particular interest and enjoyment in the automation process. A certain burst of energy arises as they tackle technical challenges and observe automated test execution. When a new feature needs to be tested, it is important to make time for it and also for QA professionals to look at it from the end-users – the customers. Test automation can lead to those situations where functionality cannot be adequately tested. At the same time, QA specialists may miss key functionality.

Web application testing, desktop and mobile QA are more common areas for QA professionals, while game testing, security and automation are more narrowly focused. Regardless of the direction you want to take, each path has its unique requirements, although many of the skills overlap.

Today, we’re going to cover the most important tips for getting started as a QA professional, according to Master Born.

1. Know the basics that QA professionals use

Before you embark on this career adventure, it would be a good idea to familiarize yourself with what others will want from you as a QA professional.

– SQL Fundamentals – Regardless of which position you choose, you should know the SQL basics, which are a must for everyone. The ability to extract data from a structured database is essential to verify that a piece of code is secure and stable.

– Knowledge of SDLC – As someone who is just starting their career or transitioning from another, different job, you need to know the software development life cycle or SLDC. Knowing the difference between Waterfall and Agile, as well as some of the Agile methodologies, such as Scrum and Kanban, can win you your dream job during an interview.

– GIT Basics – GIT is a version control system that you can use to share code with your team. GitHub, GitLab, and BitBucket are the programs that are used in most of the industry. Be sure to familiarize yourself with the basic commands and gain insight into the software.

– Soft Skills – Non-technical skills are just as important as technical skills. Highly skilled candidates who cannot work in a team face a lot of competition from less experienced candidates who can, however, work in a team. Among the most sought-after soft skills are: communication, willingness to learn, ability to prioritize, and time management.

2. Know the responsibilities of the QA specialist

A QA specialist ensures software quality before its launch. Beyond testing for bugs, they often spend up to half their time on tasks like documentation, reporting, and process improvement. Understanding test documentation terms like test case, scenario, run, cycle, and plan is crucial. Bug reporting basics are essential too, covered in certification courses. Learning tools like BrowserStack simplifies testing across various devices.

3. Information sources

Let’s not forget that the technological world is one of the fastest growing and the knowledge we have today may not be relevant tomorrow. To build a successful career as a QA professional you need to read constantly and use different but reliable sources of information. This could be different YouTube channels where there are a number of podcasts and videos on QA topics. Other sources of information include ChatGPT and Udemy, which offers several courses for testers to increase their working knowledge, which we think is worth it. There is a lot of valuable knowledge in them that are organized very well.

“The state-of-the-art open-access model improves on prior generative AI performance to increase developer productivity and provides developers equal access to the benefits of code generation AI, which in turn enables organizations of any size to more easily meet their full business potential”, said Harm de Vries, lead of ServiceNow’s StarCoder2 development team, and co-lead of BigCode.

StarCoder2 is trained on over 600 programming languages from The Stack v2 and some natural language code such as issues from Wikipedia, Arxiv and GitHub. The models use grouped query attention, a context window of 16,384 lexemes, with sliding window attention of 4,096 lexemes. Models 3B and 7B are trained on over 3 trillion lexemes, and model 15B on over 4 trillion lexemes.

According to the creators, the goal of StarCoder2 is to provide developers with features such as code generation, workflow generation, and text summarization, among others.

The project to develop StarCoder2 was led by ServiceNow and Hugging Face. StarCoder 2 is available in three distinct sizes: ServiceNow crafted a 3 billion-parameter model, Hugging Face developed a 7 billion-parameter model, and NVIDIA engineered a 15 billion-parameter model.

The smaller models are designed to offer powerful performance while using small amounts of compute power. According to the companies, the 3 billion-parameter model matches the performance of the 15 billion-parameter model of the original StarCoder release.

Read more:
1. Mark Zuckerberg Met with Japanese Prime Minister Fumio Kishida
2. Odysseus Mission Will Cease Operations
3. Meta Starts to Counter Disinformation

Kong Gateway 3.6 aims to increase developer productivity by easing the process of integrating LLMs into various products. The enhanced version provides a consistent and controllable environment for AI requests across organizations.

“Today marks a significant milestone in our journey towards democratizing AI for developers and enterprises worldwide. By open-sourcing this suite of innovative AI capabilities, including no-code AI plugins, we’re removing the barriers to AI adoption and making it possible for developers to leverage multiple LLMs effortlessly and ship AI-powered applications faster. At the same time, we’re providing governance and visibility to all the AI traffic that is being generated by an organization,“ said Marco Palladino, CTO and co-founder of Kong Inc.

The “ai-proxy” plugin enables the inclusion of multiple LLMs, such as those offered by OpenAI, Azure AI and others, providing a unified interface that allows developers to switch between models without changing their application code.

The “ai-proxy” plugin also enables the collection of detailed Layer 7 AI metrics, such as tracking the number of request and response tokens, as well as usage data across different providers and LLM models.

In addition, with the introduction of codeless AI integration plugins, most notably the “ai-request-transformer” and “ai-response-transformer”, users can embed AI capabilities directly into API requests and responses.

The “ai-prompt-template” plugin allows for centralized management of prompt templates, enabling quick updates and conformance to approved templates without the need to modify application code.

The “ai-prompt-decorator” and “ai-prompt-guard” plugins, in turn, provide mechanisms to ensure that AI prompts are both compliant with organizational policies and protected from unauthorized or sensitive content.

Read more:
1. Less than Two Months to the FISSION International Science Festival
2. Android Releases First Preview of Android 15 for Developers
3. Amazon Announces New Updates to Amazon Bedrock

“The first generation of Fortify Audit Assistant was well ahead of its time with its use of predictive analytics and machine learning,” said Prentiss Donohue, cybersecurity executive vice president at OpenText.

“Those pioneering efforts paved the way for us to derive 10 years of data from human experts and turn them into predictive models that are significantly more accurate compared to the previous generation’s models, improving efficacy in auditing by reducing false positives up to 90%. Enterprises can now leverage this depth of information—something no one else in the industry can provide—within their own software assurance programs”, he added.

With this technology, OpenText highlights the need for advanced application security tools and practices, addressing the pressure security teams face to ensure software integrity and reliability from the start.

Key updates to Fortify Audit Assistant include the ability to account for model shifts, the flexibility to learn from a company’s unique environment, expanded model expertise through language specification, and the ability to account for nuances of scan results.

The tool also aims to streamline developers’ work by allowing them to focus on addressing the most critical vulnerabilities.

According to OpenText, the advanced auditing process is a direct response to requirements for more efficient application security testing, which is too time-consuming and requires manual processing of static analysis results.

The new generation of Fortify Audit Assistant will integrate security considerations into the earliest stages of the software development lifecycle, starting right from code creation. This will help in building software systems that are not only robust and reliable, but also inherently secure.

The tool uses machine learning technology to automate the security audit process, learning from the experience of Fortify’s human auditors. This application of AI is being identified as a strategic move to address the shortage of expertise available for manual verification, which is resource intensive and impractical for many organizations.

Fortify Audit Assistant promises to significantly reduce the overhead associated with hiring teams of software engineering, computer science and cybersecurity experts, the company explains.

Read more:
1. Allen AI Institute Launches Fully Open Large Language Model
2. From Harvard Students to Users Around the World: Facebook turns 20
3. How to Invest our Money if we are Beginners?

With the new integration, ML models are immutable, traceable and secure. In addition, JFrog has enhanced its ML model management solution with new versioning capabilities, ensuring that compliance and security are an integral part of the ML model development process.

“As more companies begin managing big data in the cloud, DevOps team leaders are asking how they can scale data science and ML capabilities to accelerate software delivery without introducing risk and complexity. The combination of Artifactory and Amazon SageMaker creates a single source of truth that indoctrinates DevSecOps best practices to ML model development in the cloud – delivering flexibility, speed, security, and peace of mind – breaking into a new frontier of MLSecOps”, said Kelly Hartman, SVP of global channels and alliances at JFrog.

A curious fact
According to a Forrester survey, half of data decision makers believe that implementing governance policies within AI/ML is a major challenge to its widespread use, and 45% consider data and model security a key concern.

JFrog has effectively tackled concerns related to ML model management through its integration with Amazon SageMaker, implementing DevSecOps best practices. This integration enables developers and data scientists to expedite ML project development while upholding enterprise-level security and compliance with regulatory and organizational standards, as outlined by JFrog.

Additionally, JFrog has incorporated new versioning features into its ML Model Management solution, complementing the integration with Amazon SageMaker. These capabilities seamlessly integrate model development into an organization’s existing DevSecOps workflow, contributing to enhanced transparency regarding each version of the model. According to JFrog, this improvement significantly improves the visibility and understanding of the model versions throughout the development process.

Dropbox is integrating AI and machine self-learning into its core product to boost user efficiency. NVIDIA is providing flexible tools for Dropbox to introduce new AI capabilities, bringing more intelligence to content and workflows.

“Recent advancements in generative AI are opening up a world of possibilities to improve knowledge work and address many of the pain points we all experience around organization, prioritization, and focus,” said Drew Houston, co-founder and CEO of Dropbox.

The partnership between Dropbox and NVIDIA aims to extend Dropbox’s AI features to include personalized generative AI to improve search accuracy, streamline organization, and simplify content workflows in the cloud for millions of users.

“AI has the potential to offload routine tasks, unlock our creativity, and help us do more meaningful work. We’re excited to partner with NVIDIA and leverage their technology in new ways to deliver more personalized, AI-powered experiences to our customers”, he added.

Dropbox aims to harness NVIDIA’s advanced AI technology, encompassing AI Foundation Models, AI Enterprise software, and accelerated computing, to elevate the capabilities of its AI-driven offerings like Dropbox Dash (a versatile search tool) and Dropbox AI (which enables users to pose queries and receive summaries for extensive files).

By leveraging NVIDIA AI Foundation Models and the NeMo framework, Dropbox can tailor large language models (LLMs) to deliver personalized and pertinent information while upholding robust standards of security, privacy, and transparency.

Furthermore, Dropbox has the opportunity to enhance inference performance by employing NVIDIA AI Enterprise software, which includes Triton Inference Server and TensorRT-LLM, specifically designed for production AI applications

Red Hat introduced the official release of Red Hat Device Edge, a solution designed to facilitate the deployment of edge devices. This offering comprises a specially optimized edge operating system and a supported distribution of the Kubernetes project known as MicroShift, providing customers with two deployment choices.

“We believe the future of developer productivity depends on the continued evolution and innovation of projects like Backstage, and we’re focused on making this future a reality through contributions that help simplify, extend and accelerate the development process. Donating these plug-ins to the Backstage community is a reflection of Red Hat’s commitment to helping developers meet the demands of today as they innovate for tomorrow”, said Balaji Sivasubramanian, senior director of Developer Tools Product Management at Red Hat.

Red Hat emphasizes the advantages of Red Hat Device Edge, which encompass a minimal resource footprint, a uniform operational experience, flexible workload management, and simplified deployment processes.

Red Hat has rolled out Red Hat OpenShift 4.14, the most recent iteration of its platform. This update introduces the general availability of hosted control planes, offering a range of benefits such as reduced management expenses, quicker cluster provisioning, enhanced scalability, and increased security through the separation of control planes from workloads. According to Red Hat, the adoption of hosted control planes can result in substantial cost savings, with a potential 30% reduction in infrastructure costs and a 60% reduction in developer time.

In addition, the organization shared its contribution of five new plugins to Backstage, a framework designed for constructing developer portals. These newly added plugins are designed to integrate with technologies like Azure Container Registry, JFrog Artifactory, Kiali, Nexus, and 3scale.

Lastly, Red Hat introduced Ansible Inside, enabling developers to incorporate Ansible Playbooks within their applications. This feature caters to customers seeking to integrate automation into their applications without the need for the full spectrum of capabilities provided by the Ansible Automation Platform.

With this platform, businesses can tap into the capabilities of AI while adeptly managing the intricate challenges that organizations must conquer to deliver top-tier, secure software on a large scale.

“As companies embark on their AI adoption journey, we are seeing exponential improvements in application development. But with the vast adoption of AI code-assist tools, the question becomes, can DevSecOps processes, teams, and tools keep up with developer improvements? Businesses need to support an enhanced developer experience while overcoming roadblocks in their release pipelines, toolchains and security challenges. We have designed Denali to empower teams at every stage of the software development lifecycle (SDLC), helping to align developer outcomes with business strategy and accelerate innovation throughout the enterprise”, said Derek Holt, CEO of Digital.ai.

Denali guarantees seamless compatibility with the latest operating systems and development frameworks, elevating the performance of critical applications to new heights. Organizations can harness the potential of AI for enhanced automation of software delivery, effectively orchestrating and governing code originating from AI-assisted development. Moreover, it provides invaluable insights throughout each stage of the software delivery process, saving developers precious time and improving their access to knowledge.

“Our partnership with Digital.ai is focused on enabling secure digital transformation at leading
financial services companies. Ongoing R&D is crucial for us, and Digital.ai continually reinvests in their solution so that together, we can meet the fast-evolving needs of banking customers and help them deliver innovative applications that work for their customers. The new ARM Protection feature is an example of how Digital.ai makes application protection significantly easier while also eliminating extra steps”, said Jhonny Telles, Leadcomm’s Director of Digital Transformation.

Denali introduces a host of enhancements, including self-guided workflows, templates, and best
practices, streamlining processes and fostering alignment with quantifiable objectives. Furthermore, Denali extends its support for cloud-native application development and boasts enhanced integrations with prominent platforms like Terraform by HashiCorp, Azure Biceps, Azure Key Vault, and AWS Secret Manager.

Japan’s Fair Trade Commission also said it was examining whether Google had entered into agreements with Android smartphone makers to share ad revenue on the condition that the device maker did not install a rival search engine.

On top of all this, it will also check if Google services are prioritised on Android phones.

Google was quick to state that Android is an “open source platform that has enabled diversity” from partners and device manufacturers.

The Japanese commission will seek public input, including from third parties, until 22 November.

“Its openness and flexibility ensure that users always have a choice to customize their devices to suit their needs, including the way they browse and search the internet, or download apps,” a Google spokesperson told CNBC via email.

Android is the world’s largest mobile operating system, accounting for a roughly 80% market share of smartphones.

Google seems to have suffered a number of breaches in recent years. Some of Google’s business practices in relation to Android have come under scrutiny from regulators around the world.

Google seems to have suffered a number of breaches in recent years. Some of Google’s business practices in relation to Android have come under scrutiny from regulators around the world. In 2018, the European Union fined Google a record €4.34 billion ($4.6 billion) for abusing Android’s dominant position.

The EU said Google had unfairly favoured its own services by forcing smartphone makers to pre-install Google Chrome and Search apps bundled with the Google Play app store.

After an appeal by Google, the EU court reduced the fine imposed on Google last year.

During the trial, which began last month, the U.S. Department of Justice alleged that Google violated antitrust law through exclusive agreements with mobile phone and browser makers to make its search engine standard for consumers.

According to CNBC, this ongoing proceeding is the largest US tech antitrust trial in decades.