Artificial Intelligence (AI) and Machine Learning (ML) have significantly impacted various industries, including software development. Software testing, a crucial part of the software development lifecycle (SDLC), ensures the quality and reliability of software products. Traditionally, software testing has been a labor-intensive process requiring significant manual effort. However, the advent of AI and ML has transformed this landscape by introducing automation and intelligent decision-making capabilities. AI and ML technologies enhance the efficiency and effectiveness of software testing by automating complex tasks such as test case generation, test execution, and result analysis. These technologies reduce the time required for testing and improve the accuracy of defect detection, ultimately leading to higher quality software. AI can predict potential areas of failure by analyzing historical data and identifying patterns, which allows for more targeted and efficient testing. This paper explores the role of AI and ML in software testing by reviewing existing literature, analyzing current tools and techniques, and presenting case studies that demonstrate the practical benefits of these technologies. The literature review provides a comprehensive overview of the advancements in AI and ML applications in software testing, highlighting key methodologies and findings from various studies. The analysis of current tools showcases the capabilities of popular AI-driven testing tools such as Eggplant AI, Test.ai, Selenium, Appvance, Applitools Eyes, Katalon Studio, and Tricentis Tosca, each offering unique features and advantages. Case studies included in this paper illustrate real-world applications of AI and ML in software testing, showing significant improvements in testing efficiency, accuracy, and overall software quality.

Twitter is a microblogging service for sending short, public text messages (tweets) that has recently received more attention in scientific community. In the works of Sasaki et al. (2010) and Earle et al. (2011) the authors explored the real-time interaction on Twitter for detecting natural hazards (e.g., earthquakes, typhoons) based on the user's tweets on twitter. An inherent challenge for such an application is the natural language processing (NLP), which basically consists in converting the words in numbers (vectors and tensors) in order to (mathematically/ computationally) make predictions and classifications. Recently advanced computational tools have been made available for dealing with text computationally. In this report were implemented a NLP machine learning with TensorFlow, an end-to-end open source platform for machine learning, to process and classify events based on files containing only text.

Python is a popular programming language that has become the favored option for software developers and data scientists alike, from constructing advanced machine learning algorithms to creating easy graphical user interfaces. Python's data science skills are still being explored, especially for advanced data analysis and the creation of deep learning solutions. In this approach, Python beats other programming languages such as C . Python has a modest learning curve and is considered very beginner-friendly. But many tools must be understood to obtain the maximum benefit from Python.

AI Powered tools for test automation is the next buzzing theme in world of automation and surely this theme going to last more than decade as enterprise applications becomes complex and automation suite contains 1000s of test scripts. Biggest advantage which AI Powered tools gives compare to traditional tools like Selenium is ease of maintenance. AI Enabled Tools have in built self healing capabilities, any changes in the application let the tool heal the test scripts and this makes AI powered tools so exciting to look at. Maintaining automation suite with 100 plus test scripts never easy task even for skilled automation engineers as application tends to change with time due to ever changing business requirements and unless self healing capabilities not get added in automation life cycle any significant investment may look not very much promising for your product quality in long run. In this post, I will share some of buzzing AI Enabled tools right now in market but yes there will be strong competition in this area as enterprises look forward to enable AI in automation life cycle.

If you have ever tried to watch a live nest camera hoping to observe a falcon or other interesting bird, you may have had the experience of opening the live stream and seeing an empty nest. Not sure how long you should wait for the bird to return? In this article, I will describe my final project for the 12-week Metis Data Science Bootcamp that I attended January–March 2021. My project was aimed at automating nest monitoring for the Nottingham Trent University Falcon Cam. Using deep learning and automation tools, I designed a method for 24-hour bird detection that serves as an infrastructure upon which a Twitter bot or other notification system can be built to notify users when the bird enters or leaves the nest.

Hello & Welcome everyone after a long gap. We have been off for a while now but we are back with whole lot of interesting stuff to read & learn about. In today's post we are gonna see Web Scraping Images from Google with Selenium . This blog is in continuation to our previous blog here where we used Haarcascade and opencv for mask detection. In this post i wanted to try a deep learning approach to overcome the shortcomings of the previous blog.

Web Scraping is the process of collecting data from the internet by using various tools and frameworks. Sometimes, It is used for online price change monitoring, price comparison, and seeing how well the competitors are doing by extracting data from their websites. Web Scraping is as old as the internet is, In 1989 World wide web was launched and after four years World Wide Web Wanderer: The first web robot was created at MIT by Matthew Gray, the purpose of this crawler is to measure the size of the worldwide web. Beautiful Soup is a Python library that is used for web scraping purposes to pull the data out of HTML and XML files. It creates a parse tree from page source code that can be used to extract data in a hierarchical and more readable manner.

Web Scraping is a popular methodology to extract data from websites. This is often done to derive insights for Sentiment Analysis, Predicting User preferences, Cross-Selling products, etc. Some of the real-life examples of web scraping include – extracting data for pricing analysis, user ratings for movie sentiment analysis, corporate admin tasks to read and classify log files in an HTML, search bots trying to make sense of a results page. While web scraping activity does not provide intelligence of its own, as we have seen above the data extracted can be useful in multiple ways. A more common use case would be a start-up eCommerce website trying to set a price on its products based on market research on competitors.

This started out as a challenge. I wanted with a friend of mine to see if it was possible to build something from scratch and push it to production. In this post, we'll go through the necessary steps to build and deploy a machine learning application. This starts from data collection to deployment and the journey, as you'll see it, is exciting and fun . Before we begin, let's have a look at the app we'll be building: As you see, this web app allows a user to evaluate random brands by writing reviews.

This started out as a challenge. I wanted with a friend of mine to see if it was possible to build something from scratch and push it to production. In this post, we'll go through the necessary steps to build and deploy a machine learning application. This starts from data collection to deployment and the journey, as you'll see it, is exciting and fun . Before we begin, let's have a look at the app we'll be building: As you see, this web app allows a user to evaluate random brands by writing reviews.