Test case potency assessment is primarily a diagnostic that assess the quality of test cases. This is one of the applications of HBT (Hypothesis Based Testing). This is boutique service offering from STAG Software Private Limited.
This slide-deck from STAG software highlights good testing is to uncover those faults that have the potential to cause severe failures. The big question is “how good are the test cases to detect these faults?”
The document discusses assessing unit test quality and describes a project called PEA that aims to collect and report metrics around test directness and assertion density. It outlines various approaches tried for PEA, including static code analysis, bytecode instrumentation, aspect-oriented programming, and using a debugger. The preferred approach ended up being to use the Java Platform Debugger Architecture to monitor tests running in another JVM without modifying code.
Unit testing involves writing code to test individual units or components of an application to ensure they operate as intended. A unit test targets a specific function or class and ensures the code works correctly. A test suite is a collection of test cases that test a module. Frameworks like JUnit simplify the unit testing process and allow for test automation. JUnit tests are created in the same project structure with a "Test" suffix and use annotations to define test cases and suites. Tests can then be run and results analyzed to measure code coverage.
JMockit is a Java mocking framework that provides tools for isolating code dependencies during unit testing. It uses bytecode instrumentation to remap classes at runtime, allowing final classes and static methods to be mocked. Expectations define mock object behavior, and verifications ensure mocks are used as expected. JMockit provides a more powerful and flexible mocking approach than alternatives like Mockito through its instrumentation and expectations/verifications APIs.
This document provides an introduction to JUnit and Mockito for testing Java code. It discusses how to set up JUnit tests with annotations like @Before, @After, and @Test. It also covers using JUnit assertions and test suites. For Mockito, the document discusses how to create and use mock objects to stub behavior and verify interactions. It provides examples of argument matchers and consecutive stubbing in Mockito.
Unit Testing Concepts and Best PracticesDerek Smith
Unit testing involves writing code to test individual units or components of an application to ensure they perform as expected. The document discusses best practices for unit testing including writing atomic, consistent, self-descriptive tests with clear assertions. Tests should be separated by business module and type and not include conditional logic, loops, or exception handling. Production code should be isolated from test code. The goal of unit testing is to validate that code meets specifications and prevents regressions over time.
DejaVOO: A Regression Testing Tool for Java SoftwareManas Tungare
DejaVOO is a regression testing tool for Java that implements a safe regression test selection algorithm. The algorithm handles object-oriented features of Java like inheritance, polymorphism, and exceptions. It can select a subset of tests (T') from an original test suite (T) to validate a new version (P') of a program (P) in a way that ensures T' will expose any faults in P' while running faster than running all of T. DejaVOO analyzes programs using the Java Virtual Machine profiling interface and bytecode analysis to select only dangerous tests likely to fail. It has the potential for significant savings in regression testing time compared to re-running all tests.
Oplægget blev holdt ved et seminar i InfinIT-interessegruppen Softwaretest den 28. september 2010.
Læs mere om interessegruppen på http://www.infinit.dk/dk/interessegrupper/softwaretest/softwaretest.htm
This slide-deck from STAG software highlights good testing is to uncover those faults that have the potential to cause severe failures. The big question is “how good are the test cases to detect these faults?”
The document discusses assessing unit test quality and describes a project called PEA that aims to collect and report metrics around test directness and assertion density. It outlines various approaches tried for PEA, including static code analysis, bytecode instrumentation, aspect-oriented programming, and using a debugger. The preferred approach ended up being to use the Java Platform Debugger Architecture to monitor tests running in another JVM without modifying code.
Unit testing involves writing code to test individual units or components of an application to ensure they operate as intended. A unit test targets a specific function or class and ensures the code works correctly. A test suite is a collection of test cases that test a module. Frameworks like JUnit simplify the unit testing process and allow for test automation. JUnit tests are created in the same project structure with a "Test" suffix and use annotations to define test cases and suites. Tests can then be run and results analyzed to measure code coverage.
JMockit is a Java mocking framework that provides tools for isolating code dependencies during unit testing. It uses bytecode instrumentation to remap classes at runtime, allowing final classes and static methods to be mocked. Expectations define mock object behavior, and verifications ensure mocks are used as expected. JMockit provides a more powerful and flexible mocking approach than alternatives like Mockito through its instrumentation and expectations/verifications APIs.
This document provides an introduction to JUnit and Mockito for testing Java code. It discusses how to set up JUnit tests with annotations like @Before, @After, and @Test. It also covers using JUnit assertions and test suites. For Mockito, the document discusses how to create and use mock objects to stub behavior and verify interactions. It provides examples of argument matchers and consecutive stubbing in Mockito.
Unit Testing Concepts and Best PracticesDerek Smith
Unit testing involves writing code to test individual units or components of an application to ensure they perform as expected. The document discusses best practices for unit testing including writing atomic, consistent, self-descriptive tests with clear assertions. Tests should be separated by business module and type and not include conditional logic, loops, or exception handling. Production code should be isolated from test code. The goal of unit testing is to validate that code meets specifications and prevents regressions over time.
DejaVOO: A Regression Testing Tool for Java SoftwareManas Tungare
DejaVOO is a regression testing tool for Java that implements a safe regression test selection algorithm. The algorithm handles object-oriented features of Java like inheritance, polymorphism, and exceptions. It can select a subset of tests (T') from an original test suite (T) to validate a new version (P') of a program (P) in a way that ensures T' will expose any faults in P' while running faster than running all of T. DejaVOO analyzes programs using the Java Virtual Machine profiling interface and bytecode analysis to select only dangerous tests likely to fail. It has the potential for significant savings in regression testing time compared to re-running all tests.
Oplægget blev holdt ved et seminar i InfinIT-interessegruppen Softwaretest den 28. september 2010.
Læs mere om interessegruppen på http://www.infinit.dk/dk/interessegrupper/softwaretest/softwaretest.htm
This outlines FIVE key application scenarios of validation using doSmartQA, a smart probing assistant to test deeply & rapidly.
It facilitates rapid testing in short sessions of Recon, Explore & Recoup, based on HyBIST -
‘Hypothesis Based Immersive Session Testing’, an intellectual practice of probing.
“Despite all the testing we do, field issues do not seem to abate. Sometimes it is a few serious issues that cause us to react intensely, sometimes it is a bunch of simple issues that make us consume bandwidth. Clearly the backlog is building up, with debts to be serviced, straining capacity to deliver new ideas.”
This is what I hear from senior engineering managers of product companies. How do you go about fixing this? Well, I have seen a flurry of activity to identify root cause(s) and address them. They help to set focus, but fizzle out.
Analysing 'quality of technical debt’ to understand types of issues that leak enables practical actions, rather than jumping into the ‘reason of why’ (root cause). Smart QA it is, to do failure analytics differently, to ‘tighten the purse’.
Technical debt is indeed a serious drain on engineering capacity, forcing one to fix issues at the expense of building revenue yielding new features. Smart failure analytics visualises problems well, enabling clear actions to strengthen practice and reduce debt significantly.
If you are “choked by technical debt”, then you may find our SmartQA consulting (stagsoftware.com/smartqa) interesting, where we unshackle your practice so that you can exploit technology.
"We track a lot of metrics related to progress of development and quality every sprint, like backlogs, technical debt, velocity, task status etc. What is not very evident is the 'quality of movement' i.e. how well done, so that we create less debt as we move. How can I get a better insight of the quality of tests done and a more objective measure of product quality?"
Extrinsic metrics are easier to measure and give visibility of direction, progress, speed and external feel of product quality. Intrinsic metrics are deeper, harder to measure but can give greater insight into the quality of work. Measuring this requires a good structure and organisation of test artefacts. The benefit - a greater insight into effectiveness of outcome and therefore lower technical debt & greater acceleration, don't you think?
Metrics can be classified as measuring work progress, work quality, product quality and practice quality. Except for the first one on work progress where we have a lot of measures facilitated by project and test management tools, the others depend on test organisation and clarity of types of issues to uncover. 'Quality Levels' based on HBT (Hypothesis Based Testing) provides a strong foundation for these, enabling you to assess potential test effectiveness, judge product quality objectively and fine tune practice quality .
If you are keen on "insightful quality metrics", then you may find our SmartQA consulting (stagsoftware.com/smartqa) interesting, where we unshackle your practice so that you can see clearly and do far better.
“As we embrace faster release cycles, testing has become a bottleneck. Yes, we have embraced automation as the way forward. We have a huge regression suite and therefore a big backlog for automation, a tough balance to speed up and yet maintain the fast paced release rhythm. What can I do?”
Automated tests are great to monitor a system’s health. Rather than just use regression as the candidate for automation, key flows that signify the pulse of a system's health are superior, don’t you think? And, this won’t create a huge backlog for automation, right?
Most often I have seen automation embraced as the solution to speed up testing. Conceptually correct it is, the problem is - what makes it worth the while to automate? Automated tests have to be in sync with the product and are therefore not a one time effort.
Choosing the right ones implies, it needs to be at the level of user flow, and be a clear indicator of health. Unless test scenarios are well structured and organised, choosing the right ones will turn out to be difficult, and ultimately weigh you down. It then becomes a pursuit of catching up with automation rather than making it work for you.
The goal is not 100% automation, it really is no leakage of defects. Automated tests are really ‘checks’ that assess key paths for good health (correctness) while intelligent human tests are focused on finding issues(robustness). A harmonious balance between these two enables clean code to be delivered without being weighed down by automation.
If you are “weighed down by automation“, then you may find our SmartQA consulting (stagsoftware.com/smartqa) interesting, where we unshackle your practice so that you can exploit technology.
Inspired by how the world is handling Covid19, this slideshare lists actions taken and criteria met to contain the pandemic and correlate this to how we can deliver clean code for large scale software systems. This article focuses on the process flow and criteria for delivering clean code.
The document outlines 7 thinking tools to help with rapid testing:
1. Landscaper - Do a survey to understand the big picture
2. Persona map - Map out who uses what
3. Scope map - Map out user expectations
4. Interaction map - Map what may affect what
5. Environment map - Map test environments
6. Scenario creator - Create test scenarios
7. Dashboard - Stop, analyze, and refine
These tools are part of an immersive session testing approach using reconnaissance, exploration, and rest/recovery phases to facilitate rapid yet thorough scientific exploration. A related SaaS tool called doSmartQA will offer these tools and interested users can email the founder for
Agile and automation have been great enablers to doing tests faster. How we can accelerate further to accomplish more by doing less is the objective of this webinar.
“Left-shifting” by smart decomposition of dev testing aided by smart lightweight aids to perform rapid dev testing will be the takeaways of this webinar.
Three ideas to regression test smarter and outline THREE AIDS to do this.
AID #1: Fault propagation analyser - Figure out how what-to-retest by doing a smarter impact analysis using a scientific approach to understanding fault propagation due to change.
AID #2 : Automation analyser - Ensure scenarios are fit-to-automate so that they are easily scriptable and easily maintainable
AID #3 : Yield analyser : Figure out how much not to regress by analysing defect yields over time to understand what parts of the system have been hardened
Well, automation is an obvious choice, ensure that the scenarios are “fit enough for automation” so that you don’t end spend much effort maintaining the scripts to be in sync with every change.
Drawing inspiration from Atul Gawande's book "The checklist manifesto", T Ashok, CEO, STAG Software, explores at how we can exploit the power of checklist to delivering good quality code.
Drawing inspiration from Atul Gawande's book "The checklist manifesto", T Ashok, CEO, STAG Software, explores at how we can exploit the power of checklist to delivering good quality code.
This is the webinar recording on the topic ‘Test Case Immunity’- Optimize testing. In this webinar we have conveyed an interesting idea of measuring “Test Case Immunity” to logically assess what test cases to drop by so that we can 'do none'
This document outlines a structured and scientific approach to designing tests for user stories. It discusses four types of entities to test: individual user stories, sets of user stories for an epic, sets of user stories that form a flow, and sets of user stories across releases. It also describes eight levels of quality to test for. The approach involves first understanding what to test and what criteria to test for. Test cases are then designed using techniques like thinking and proving correctness statically or executing and evaluating dynamically. Conditions that govern behavior are extracted to develop test scenarios that stimulate different behaviors.
This document discusses how to establish a clear baseline for testing user stories. It defines a baseline as a cartesian product of "what to test" and "test for what". What to test includes individual user stories and collections of user stories spanning epics. Test for what refers to acceptance criteria such as functionality, performance, security, and usability. Different types of tests are mapped to these criteria. Together, what to test and test for what form the baseline, and applying strategies like thinking and proving or executing and evaluating tests establishes a clear approach to validating user stories.
Part1 of Tri-webinar series consisting of three webinars commencing with 'How-to question to understand an user story and identify gaps', moving onto 'How-to set clear baseline' to ensure an effective strategy, and finally culminating with 'How-to design test scenarios/cases' using a scientific and disciplined approach
"Language shapes the way you think" was the topic of the talk presented by T Ashok, CEO STAG Software, to a group of test professionals at a Pune-based IT services and solutions provider on June 16, 2014.
The document describes STAG Software's HBT Quality Visualization Tool. The tool allows users to assess quality across three areas in 3 sentences:
1) Are test assets good? It evaluates the quality of test cases by analyzing factors like applicable test types, test case counts by importance and quality level, and positive/negative ratios.
2) Have we assessed completely? It measures the quality of execution by analyzing execution metrics like percentages completed by test, entity, quality level, and progress over cycles.
3) How good are the outcomes? It determines the quality of the product/application by calculating a cleanliness index based on passed and total test cases, and analyzing performance by entity, cleanliness criteria, and
This presentation on Hypothesis Based Testing (HBT) was delivered by Mr Satvik Kini, Associate Quality Manager, Suite Test Centre, SAP Labs India Pvt. Ltd at STeP-IN Forum webinar on Dec 19, 2013.
The document outlines an approach called "Descriptive-Prescriptive" for better problem solving. It involves first describing a problem by connecting elements and details to understand it fully ("Analysis"), then prescribing rules and conditions to formulate a solution ("Synthesis"). This approach can be applied to test baselining, strategy formulation, test design, and reporting. Diagrams and examples are provided to illustrate applying description and prescription at different stages. The approach forms the basis of a personal test methodology called HBT, which uses six stages and eight disciplines of thinking.
The document describes a three-step approach to improving defect yield in testing:
1. Conduct a potency assessment to determine which types of defects are being targeted by current test cases and if any important types are missing.
2. Perform potential defect type re-targeting to add new test cases to cover additional defect types identified as missing.
3. Enhance existing test cases through potency improvement to ensure they are complete in uncovering defects.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
This outlines FIVE key application scenarios of validation using doSmartQA, a smart probing assistant to test deeply & rapidly.
It facilitates rapid testing in short sessions of Recon, Explore & Recoup, based on HyBIST -
‘Hypothesis Based Immersive Session Testing’, an intellectual practice of probing.
“Despite all the testing we do, field issues do not seem to abate. Sometimes it is a few serious issues that cause us to react intensely, sometimes it is a bunch of simple issues that make us consume bandwidth. Clearly the backlog is building up, with debts to be serviced, straining capacity to deliver new ideas.”
This is what I hear from senior engineering managers of product companies. How do you go about fixing this? Well, I have seen a flurry of activity to identify root cause(s) and address them. They help to set focus, but fizzle out.
Analysing 'quality of technical debt’ to understand types of issues that leak enables practical actions, rather than jumping into the ‘reason of why’ (root cause). Smart QA it is, to do failure analytics differently, to ‘tighten the purse’.
Technical debt is indeed a serious drain on engineering capacity, forcing one to fix issues at the expense of building revenue yielding new features. Smart failure analytics visualises problems well, enabling clear actions to strengthen practice and reduce debt significantly.
If you are “choked by technical debt”, then you may find our SmartQA consulting (stagsoftware.com/smartqa) interesting, where we unshackle your practice so that you can exploit technology.
"We track a lot of metrics related to progress of development and quality every sprint, like backlogs, technical debt, velocity, task status etc. What is not very evident is the 'quality of movement' i.e. how well done, so that we create less debt as we move. How can I get a better insight of the quality of tests done and a more objective measure of product quality?"
Extrinsic metrics are easier to measure and give visibility of direction, progress, speed and external feel of product quality. Intrinsic metrics are deeper, harder to measure but can give greater insight into the quality of work. Measuring this requires a good structure and organisation of test artefacts. The benefit - a greater insight into effectiveness of outcome and therefore lower technical debt & greater acceleration, don't you think?
Metrics can be classified as measuring work progress, work quality, product quality and practice quality. Except for the first one on work progress where we have a lot of measures facilitated by project and test management tools, the others depend on test organisation and clarity of types of issues to uncover. 'Quality Levels' based on HBT (Hypothesis Based Testing) provides a strong foundation for these, enabling you to assess potential test effectiveness, judge product quality objectively and fine tune practice quality .
If you are keen on "insightful quality metrics", then you may find our SmartQA consulting (stagsoftware.com/smartqa) interesting, where we unshackle your practice so that you can see clearly and do far better.
“As we embrace faster release cycles, testing has become a bottleneck. Yes, we have embraced automation as the way forward. We have a huge regression suite and therefore a big backlog for automation, a tough balance to speed up and yet maintain the fast paced release rhythm. What can I do?”
Automated tests are great to monitor a system’s health. Rather than just use regression as the candidate for automation, key flows that signify the pulse of a system's health are superior, don’t you think? And, this won’t create a huge backlog for automation, right?
Most often I have seen automation embraced as the solution to speed up testing. Conceptually correct it is, the problem is - what makes it worth the while to automate? Automated tests have to be in sync with the product and are therefore not a one time effort.
Choosing the right ones implies, it needs to be at the level of user flow, and be a clear indicator of health. Unless test scenarios are well structured and organised, choosing the right ones will turn out to be difficult, and ultimately weigh you down. It then becomes a pursuit of catching up with automation rather than making it work for you.
The goal is not 100% automation, it really is no leakage of defects. Automated tests are really ‘checks’ that assess key paths for good health (correctness) while intelligent human tests are focused on finding issues(robustness). A harmonious balance between these two enables clean code to be delivered without being weighed down by automation.
If you are “weighed down by automation“, then you may find our SmartQA consulting (stagsoftware.com/smartqa) interesting, where we unshackle your practice so that you can exploit technology.
Inspired by how the world is handling Covid19, this slideshare lists actions taken and criteria met to contain the pandemic and correlate this to how we can deliver clean code for large scale software systems. This article focuses on the process flow and criteria for delivering clean code.
The document outlines 7 thinking tools to help with rapid testing:
1. Landscaper - Do a survey to understand the big picture
2. Persona map - Map out who uses what
3. Scope map - Map out user expectations
4. Interaction map - Map what may affect what
5. Environment map - Map test environments
6. Scenario creator - Create test scenarios
7. Dashboard - Stop, analyze, and refine
These tools are part of an immersive session testing approach using reconnaissance, exploration, and rest/recovery phases to facilitate rapid yet thorough scientific exploration. A related SaaS tool called doSmartQA will offer these tools and interested users can email the founder for
Agile and automation have been great enablers to doing tests faster. How we can accelerate further to accomplish more by doing less is the objective of this webinar.
“Left-shifting” by smart decomposition of dev testing aided by smart lightweight aids to perform rapid dev testing will be the takeaways of this webinar.
Three ideas to regression test smarter and outline THREE AIDS to do this.
AID #1: Fault propagation analyser - Figure out how what-to-retest by doing a smarter impact analysis using a scientific approach to understanding fault propagation due to change.
AID #2 : Automation analyser - Ensure scenarios are fit-to-automate so that they are easily scriptable and easily maintainable
AID #3 : Yield analyser : Figure out how much not to regress by analysing defect yields over time to understand what parts of the system have been hardened
Well, automation is an obvious choice, ensure that the scenarios are “fit enough for automation” so that you don’t end spend much effort maintaining the scripts to be in sync with every change.
Drawing inspiration from Atul Gawande's book "The checklist manifesto", T Ashok, CEO, STAG Software, explores at how we can exploit the power of checklist to delivering good quality code.
Drawing inspiration from Atul Gawande's book "The checklist manifesto", T Ashok, CEO, STAG Software, explores at how we can exploit the power of checklist to delivering good quality code.
This is the webinar recording on the topic ‘Test Case Immunity’- Optimize testing. In this webinar we have conveyed an interesting idea of measuring “Test Case Immunity” to logically assess what test cases to drop by so that we can 'do none'
This document outlines a structured and scientific approach to designing tests for user stories. It discusses four types of entities to test: individual user stories, sets of user stories for an epic, sets of user stories that form a flow, and sets of user stories across releases. It also describes eight levels of quality to test for. The approach involves first understanding what to test and what criteria to test for. Test cases are then designed using techniques like thinking and proving correctness statically or executing and evaluating dynamically. Conditions that govern behavior are extracted to develop test scenarios that stimulate different behaviors.
This document discusses how to establish a clear baseline for testing user stories. It defines a baseline as a cartesian product of "what to test" and "test for what". What to test includes individual user stories and collections of user stories spanning epics. Test for what refers to acceptance criteria such as functionality, performance, security, and usability. Different types of tests are mapped to these criteria. Together, what to test and test for what form the baseline, and applying strategies like thinking and proving or executing and evaluating tests establishes a clear approach to validating user stories.
Part1 of Tri-webinar series consisting of three webinars commencing with 'How-to question to understand an user story and identify gaps', moving onto 'How-to set clear baseline' to ensure an effective strategy, and finally culminating with 'How-to design test scenarios/cases' using a scientific and disciplined approach
"Language shapes the way you think" was the topic of the talk presented by T Ashok, CEO STAG Software, to a group of test professionals at a Pune-based IT services and solutions provider on June 16, 2014.
The document describes STAG Software's HBT Quality Visualization Tool. The tool allows users to assess quality across three areas in 3 sentences:
1) Are test assets good? It evaluates the quality of test cases by analyzing factors like applicable test types, test case counts by importance and quality level, and positive/negative ratios.
2) Have we assessed completely? It measures the quality of execution by analyzing execution metrics like percentages completed by test, entity, quality level, and progress over cycles.
3) How good are the outcomes? It determines the quality of the product/application by calculating a cleanliness index based on passed and total test cases, and analyzing performance by entity, cleanliness criteria, and
This presentation on Hypothesis Based Testing (HBT) was delivered by Mr Satvik Kini, Associate Quality Manager, Suite Test Centre, SAP Labs India Pvt. Ltd at STeP-IN Forum webinar on Dec 19, 2013.
The document outlines an approach called "Descriptive-Prescriptive" for better problem solving. It involves first describing a problem by connecting elements and details to understand it fully ("Analysis"), then prescribing rules and conditions to formulate a solution ("Synthesis"). This approach can be applied to test baselining, strategy formulation, test design, and reporting. Diagrams and examples are provided to illustrate applying description and prescription at different stages. The approach forms the basis of a personal test methodology called HBT, which uses six stages and eight disciplines of thinking.
The document describes a three-step approach to improving defect yield in testing:
1. Conduct a potency assessment to determine which types of defects are being targeted by current test cases and if any important types are missing.
2. Perform potential defect type re-targeting to add new test cases to cover additional defect types identified as missing.
3. Enhance existing test cases through potency improvement to ensure they are complete in uncovering defects.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/