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The document discusses how to make automation an asset to software testing organizations by outlining the advantages and disadvantages of manual versus automated testing, providing examples of what types of tests are best suited for automation, and describing best practices for developing an effective test automation process and addressing common myths about automation. It emphasizes that automation can increase testing efficiency and coverage but requires proper planning, resources, and maintenance to be successful.

Consumers want their applications to work perfectly each time. If your app is rife with performance issues, bugs, and glitches, it can lose its user base in no time due to its flawed quality. As a result, testing your software product is imperative to ensure project teams check, verify, and validate the functionality of the developed software and deliver optimal user experiences.

This document discusses automation testing. It begins by defining automation testing and listing its benefits, which include saving time and money, improving accuracy, and increasing test coverage. It then covers levels of automation testing, frameworks, approaches like record and playback, modular scripting, and keyword-driven testing. The document also discusses the automation testing lifecycle, how to choose a testing tool, types of tools, when to automate and who should automate, supporting practices, and skills needed for automation testing.

Automated software testing is critical to success in today’s competitive business climate. As test automation specialists, TestingXperts, offers a comprehensive range of test automation services to meet your goal of increasing velocity throughout the software development lifecycle.

The document discusses test automation, including defining it as using special software to control test execution and compare results. It lists major objectives like time and cost savings, improved productivity, accuracy, and coverage. It recommends automating repetitive, tedious, time-consuming, or high-risk tests. A typical automation process includes planning, design, tool development, deployment, and review. Choosing an automation tool requires considering ease of use, supported test types, and maintenance. Automated testing reduces long-term costs while manual testing has shorter-term benefits like more bugs found through hands-on testing.

Hopper's approach to QA is described in the Case study. At Hopper, we believe that QA starts at the very beginning of product life cycle. This helps reduce risk and deliver quality products. We combine all aspects of QA - blackbox testing, performance testing, load testing, regression testing, QA Automation etc. We also design QA systems where the existing frameworks may not work.

Automation testing involves developing and executing tests that can run unattended, comparing actual and expected results. The major objectives of automation testing are to speed up testing, reduce costs and time, and increase quality. Automation testing is faster than manual testing and can test more scenarios. However, high upfront investment is required for tools and training. Both manual and automation testing are needed as automation does not cover all test cases. Popular automation testing tools include Selenium, QTP, and TestComplete.

This is chapter 4 of ISTQB Specialist Mobile Application Tester certification. This presentation helps aspirants understand and prepare the content of the certification.

Continuous testing is one of the most effective ways of building quality into the continuous delivery pipeline; yet it is difficult to implement in practice. Continuous testing involves more than test automation. Although test automation is a must; continuous risk analysis and optimizing the test suite is critical so that test automation doesn’t become a bottleneck in the DevOps pipeline. In this presentation, you’ll learn how to implement an effective continuous test strategy throughout the continuous delivery pipeline.

This document discusses best practices for developing an automated testing framework. It recommends using a hybrid keyword-driven and data-driven approach to reduce scripting efforts. Some key points covered include the benefits of automation like reduced costs and increased speed/accuracy over manual testing. It also discusses factors to consider when selecting an automation tool, common challenges, and provides an example case study showing the ROI achieved through automation. Best practices emphasized include loose coupling of framework components, reuse of generic libraries, and treating framework development as a distinct project.

This presentation was originally given at Quality Jam London. Elise covered test automation and the progression for test automation that you might encounter. The session agenda included: The stages of the test team Why are we automating? What are we automating? How are we automating? What languages should we use? What frameworks and libraries should we use? Open source or proprietary? Learn more at www.qualityjam.com

The document discusses top pitfalls of test automation and how to avoid them. It identifies the top 5 pitfalls as: 1) diving into open source tools without preparation, 2) developing test scripts without standardization, 3) automating all test cases without prioritization, 4) choosing in-house testing over cloud options, and 5) assuming automation testing is not the tester's job. It provides guidance on how to effectively implement test automation by choosing the right tools, standardizing test development, prioritizing test cases, leveraging cloud options, and defining tester responsibilities.

Rapid automation testing is a type of app testing technique that utilizes automation tools to execute tests quickly and efficiently.

This document discusses software testing and provides details on various testing methodologies. It defines software testing as evaluating a system or component to determine if it meets specified requirements. The document then outlines the testing process, issues to consider like quality standards and strategy, and different testing types like manual and automated testing. It provides information on planning, designing, and implementing various test cases and describes tools that can be used for automated testing.

This document provides an overview of testing in the agile environment and automated testing tools. It discusses agile testing methodologies like test-driven development, acceptance test-driven development, and behavior-driven development. It also covers testing web and mobile applications. The document then focuses on automated testing, describing how it works and the types of tests that are normally automated. It discusses benefits and best practices of automated testing. Finally, it covers popular automated testing tools like Selenium, Katalon Studio, and Cucumber and provides pros and cons of each.

In this quality assurance training session, you will learn introduction to automation testing. Topics covered in this course are: • Introduction • Why Automated Testing? • What can I Automate? • Test Automation Process • Automation Tool • Automation Framework To know more, visit this link: https://www.mindsmapped.com/courses/quality-assurance/software-testing-quality-assurance-qa-training-with-hands-on-exercises/

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Syllabus

Introduction to information retrieval, Major challenges in IR

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Image recognition, which comes under Artificial Intelligence (AI) is a critical aspect of computer vision, enabling computers or other computing devices to identify and categorize objects within images. Among numerous fields of life, food processing is an important area, in which image processing plays a vital role, both for producers and consumers. This study focuses on the binary classification of strawberries, where images are sorted into one of two categories. We Utilized a dataset of strawberry images for this study; we aim to determine the effectiveness of different models in identifying whether an image contains strawberries. This research has practical applications in fields such as agriculture and quality control. We compared various popular deep learning models, including MobileNetV2, Convolutional Neural Networks (CNN), and DenseNet121, for binary classification of strawberry images. The accuracy achieved by MobileNetV2 is 96.7%, CNN is 99.8%, and DenseNet121 is 93.6%. Through rigorous testing and analysis, our results demonstrate that CNN outperforms the other models in this task. In the future, the deep learning models can be evaluated on a richer and larger number of images (datasets) for better/improved results.

The rapid advancements in artificial intelligence and natural language processing have significantly transformed human-computer interactions. This thesis presents the design, development, and evaluation of an intelligent chatbot capable of engaging in natural and meaningful conversations with users. The chatbot leverages state-of-the-art deep learning techniques, including transformer-based architectures, to understand and generate human-like responses. Key contributions of this research include the implementation of a context- aware conversational model that can maintain coherent dialogue over extended interactions. The chatbot's performance is evaluated through both automated metrics and user studies, demonstrating its effectiveness in various applications such as customer service, mental health support, and educational assistance. Additionally, ethical considerations and potential biases in chatbot responses are examined to ensure the responsible deployment of this technology. The findings of this thesis highlight the potential of intelligent chatbots to enhance user experience and provide valuable insights for future developments in conversational AI.

The iMMS is a unique application that is synchronizing both user experience and copyrights while providing services like online music management, legal downloads, artists’ management. There are several other applications available in the market that either provides some specific services or large scale integrated solutions. Our product differs from the rest in a way that we give more power to the users remaining within the copyrights circle.

Pre-trained Large Language Models (LLM) have achieved remarkable successes in several domains. However, code-oriented LLMs are often heavy in computational complexity, and quadratically with the length of the input code sequence. Toward simplifying the input program of an LLM, the state-of-the-art approach has the strategies to filter the input code tokens based on the attention scores given by the LLM. The decision to simplify the input program should not rely on the attention patterns of an LLM, as these patterns are influenced by both the model architecture and the pre-training dataset. Since the model and dataset are part of the solution domain, not the problem domain where the input program belongs, the outcome may differ when the model is trained on a different dataset. We propose SlimCode, a model-agnostic code simplification solution for LLMs that depends on the nature of input code tokens. As an empirical study on the LLMs including CodeBERT, CodeT5, and GPT-4 for two main tasks: code search and summarization. We reported that 1) the reduction ratio of code has a linear-like relation with the saving ratio on training time, 2) the impact of categorized tokens on code simplification can vary significantly, 3) the impact of categorized tokens on code simplification is task-specific but model-agnostic, and 4) the above findings hold for the paradigm–prompt engineering and interactive in-context learning and this study can save reduce the cost of invoking GPT-4 by 24%per API query. Importantly, SlimCode simplifies the input code with its greedy strategy and can obtain at most 133 times faster than the state-of-the-art technique with a significant improvement. This paper calls for a new direction on code-based, model-agnostic code simplification solutions to further empower LLMs.

In the realm of Android development, the main thread is our stage, but too often, it becomes a battleground where performance issues arise, leading to ANRS, frozen frames, and sluggish Uls. As we strive for excellence in user experience, understanding and optimizing the main thread becomes essential to prevent these common perforrmance bottlenecks. We have strategies and best practices for keeping the main thread uncluttered. We'll examine the root causes of performance issues and techniques for monitoring and improving main thread health as wel as app performance. In this talk, participants will walk away with practical knowledge on enhancing app performance by mastering the main thread. We'll share proven approaches to eliminate real-life ANRS and frozen frames to build apps that deliver butter smooth experience.

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This study aimed to profile the coffee shops in Talavera, Nueva Ecija, to develop a standardized checklist for aspiring entrepreneurs. The researchers surveyed 10 coffee shop owners in the municipality of Talavera. Through surveys, the researchers delved into the Owner's Demographic, Business details, Financial Requirements, and other requirements needed to consider starting up a coffee shop. Furthermore, through accurate analysis, the data obtained from the coffee shop owners are arranged to derive key insights. By analyzing this data, the study identifies best practices associated with start-up coffee shops’ profitability in Talavera. These findings were translated into a standardized checklist outlining essential procedures including the lists of equipment needed, financial requirements, and the Traditional and Social Media Marketing techniques. This standardized checklist served as a valuable tool for aspiring and existing coffee shop owners in Talavera, streamlining operations, ensuring consistency, and contributing to business success.

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