Python unit testing
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This document discusses unit testing in Python. It explains that unit tests should be written before code to automatically test inputs and outputs. The code is then written to pass the tests. Tests are implemented by creating test classes that inherit from unittest.TestCase and adding test methods with assert statements. The document provides an example test and steps for creating unit tests, including importing unittest and the code to test.
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TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...Andre Hora Unittest and pytest are the most popular testing frameworks in Python. Overall, pytest provides some advantages, including simpler assertion, reuse of fixtures, and interoperability. Due to such benefits, multiple projects in the Python ecosystem have migrated from unittest to pytest. To facilitate the migration, pytest can also run unittest tests, thus, the migration can happen gradually over time. However, the migration can be timeconsuming and take a long time to conclude. In this context, projects would benefit from automated solutions to support the migration process. In this paper, we propose TestMigrationsInPy, a dataset of test migrations from unittest to pytest. TestMigrationsInPy contains 923 real-world migrations performed by developers. Future research proposing novel solutions to migrate frameworks in Python can rely on TestMigrationsInPy as a ground truth. Moreover, as TestMigrationsInPy includes information about the migration type (e.g., changes in assertions or fixtures), our dataset enables novel solutions to be verified effectively, for instance, from simpler assertion migrations to more complex fixture migrations. TestMigrationsInPy is publicly available at: https://github.com/altinoalvesjunior/TestMigrationsInPy.
Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...
Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.
How Valletta helped healthcare SaaS to transform QA and compliance to grow wi...
How Valletta helped healthcare SaaS to transform QA and compliance to grow wi...Egor Kaleynik This case study explores how we partnered with a mid-sized U.S. healthcare SaaS provider to help them scale from a successful pilot phase to supporting over 10,000 users—while meeting strict HIPAA compliance requirements.
Faced with slow, manual testing cycles, frequent regression bugs, and looming audit risks, their growth was at risk. Their existing QA processes couldn’t keep up with the complexity of real-time biometric data handling, and earlier automation attempts had failed due to unreliable tools and fragmented workflows.
We stepped in to deliver a full QA and DevOps transformation. Our team replaced their fragile legacy tests with Testim’s self-healing automation, integrated Postman and OWASP ZAP into Jenkins pipelines for continuous API and security validation, and leveraged AWS Device Farm for real-device, region-specific compliance testing. Custom deployment scripts gave them control over rollouts without relying on heavy CI/CD infrastructure.
The result? Test cycle times were reduced from 3 days to just 8 hours, regression bugs dropped by 40%, and they passed their first HIPAA audit without issue—unlocking faster contract signings and enabling them to expand confidently. More than just a technical upgrade, this project embedded compliance into every phase of development, proving that SaaS providers in regulated industries can scale fast and stay secure.
How to Batch Export Lotus Notes NSF Emails to Outlook PST Easily?
How to Batch Export Lotus Notes NSF Emails to Outlook PST Easily?steaveroggers Migrating from Lotus Notes to Outlook can be a complex and time-consuming task, especially when dealing with large volumes of NSF emails. This presentation provides a complete guide on how to batch export Lotus Notes NSF emails to Outlook PST format quickly and securely. It highlights the challenges of manual methods, the benefits of using an automated tool, and introduces eSoftTools NSF to PST Converter Software — a reliable solution designed to handle bulk email migrations efficiently. Learn about the software’s key features, step-by-step export process, system requirements, and how it ensures 100% data accuracy and folder structure preservation during migration. Make your email transition smoother, safer, and faster with the right approach.
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Secure Test Infrastructure: The Backbone of Trustworthy Software Development
Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi A secure test infrastructure ensures that the testing process doesn’t become a gateway for vulnerabilities. By protecting test environments, data, and access points, organizations can confidently develop and deploy software without compromising user privacy or system integrity.
Scaling GraphRAG: Efficient Knowledge Retrieval for Enterprise AI
Scaling GraphRAG: Efficient Knowledge Retrieval for Enterprise AIdanshalev If we were building a GenAI stack today, we'd start with one question: Can your retrieval system handle multi-hop logic?
Trick question, b/c most can’t. They treat retrieval as nearest-neighbor search.
Today, we discussed scaling #GraphRAG at AWS DevOps Day, and the takeaway is clear: VectorRAG is naive, lacks domain awareness, and can’t handle full dataset retrieval.
GraphRAG builds a knowledge graph from source documents, allowing for a deeper understanding of the data + higher accuracy.
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Adobe Master Collection CC (Creative Cloud) is a comprehensive subscription-based package that bundles virtually all of Adobe's creative software applications. It provides access to a wide range of tools for graphic design, video editing, web development, photography, and more. Essentially, it's a one-stop-shop for creatives needing a broad set of professional tools.
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Salesforce Data Cloud- Hyperscale data platform, built for Salesforce.
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Meet the Agents: How AI Is Learning to Think, Plan, and Collaborate
Meet the Agents: How AI Is Learning to Think, Plan, and CollaborateMaxim Salnikov Imagine if apps could think, plan, and team up like humans. Welcome to the world of AI agents and agentic user interfaces (UI)! In this session, we'll explore how AI agents make decisions, collaborate with each other, and create more natural and powerful experiences for users.
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Requirements in Engineering AI- Enabled Systems: Open Problems and Safe AI Sy...
Requirements in Engineering AI- Enabled Systems: Open Problems and Safe AI Sy...Lionel Briand Keynote at RAISE workshop, ICSE 2025
Societal challenges of AI: biases, multilinguism and sustainability
Societal challenges of AI: biases, multilinguism and sustainabilityJordi Cabot Towards a fairer, inclusive and sustainable AI that works for everybody.
Reviewing the state of the art on these challenges and what we're doing at LIST to test current LLMs and help you select the one that works best for you
Landscape of Requirements Engineering for/by AI through Literature Review
Landscape of Requirements Engineering for/by AI through Literature ReviewHironori Washizaki Hironori Washizaki, "Landscape of Requirements Engineering for/by AI through Literature Review," RAISE 2025: Workshop on Requirements engineering for AI-powered SoftwarE, 2025.
Not So Common Memory Leaks in Java Webinar
Not So Common Memory Leaks in Java WebinarTier1 app This SlideShare presentation is from our May webinar, “Not So Common Memory Leaks & How to Fix Them?”, where we explored lesser-known memory leak patterns in Java applications. Unlike typical leaks, subtle issues such as thread local misuse, inner class references, uncached collections, and misbehaving frameworks often go undetected and gradually degrade performance. This deck provides in-depth insights into identifying these hidden leaks using advanced heap analysis and profiling techniques, along with real-world case studies and practical solutions. Ideal for developers and performance engineers aiming to deepen their understanding of Java memory management and improve application stability.
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"YouTube by Click" likely refers to the ByClick Downloader software, a video downloading and conversion tool, specifically designed to download content from YouTube and other video platforms. It allows users to download YouTube videos for offline viewing and to convert them to different formats.
Mastering Fluent Bit: Ultimate Guide to Integrating Telemetry Pipelines with ...
Mastering Fluent Bit: Ultimate Guide to Integrating Telemetry Pipelines with ...Eric D. Schabell It's time you stopped letting your telemetry data pressure your budgets and get in the way of solving issues with agility! No more I say! Take back control of your telemetry data as we guide you through the open source project Fluent Bit. Learn how to manage your telemetry data from source to destination using the pipeline phases covering collection, parsing, aggregation, transformation, and forwarding from any source to any destination. Buckle up for a fun ride as you learn by exploring how telemetry pipelines work, how to set up your first pipeline, and exploring several common use cases that Fluent Bit helps solve. All this backed by a self-paced, hands-on workshop that attendees can pursue at home after this session (https://o11y-workshops.gitlab.io/workshop-fluentbit).
Explaining GitHub Actions Failures with Large Language Models Challenges, In...
Explaining GitHub Actions Failures with Large Language Models Challenges, In...ssuserb14185 GitHub Actions (GA) has become the de facto tool that developers use to automate software workflows, seamlessly building, testing, and deploying code. Yet when GA fails, it disrupts development, causing delays and driving up costs. Diagnosing failures becomes especially challenging because error logs are often long, complex and unstructured. Given these difficulties, this study explores the potential of large language models (LLMs) to generate correct, clear, concise, and actionable contextual descriptions (or summaries) for GA failures, focusing on developers’ perceptions of their feasibility and usefulness. Our results show that over 80% of developers rated LLM explanations positively in terms of correctness for simpler/small logs. Overall, our findings suggest that LLMs can feasibly assist developers in understanding common GA errors, thus, potentially reducing manual analysis. However, we also found that improved reasoning abilities are needed to support more complex CI/CD scenarios. For instance, less experienced developers tend to be more positive on the described context, while seasoned developers prefer concise summaries. Overall, our work offers key insights for researchers enhancing LLM reasoning, particularly in adapting explanations to user expertise.
https://arxiv.org/abs/2501.16495
Python unit testing
- 1. D. C. Sherman A UnitTesting FrameworkA UnitTesting Framework for Pythonfor Python
- 2. Traditional approach: Plan out the tests on paper Manually conduct the tests If there is a failure, fix the code and repeat the tests How to test your code?How to test your code?
- 3. Before writing any Python code you first write an automated test, with particular focus on the correct input and output for your code. At this stage you are not concerned with how you implement your code. Run your test and let it fail.This is OK, as you have yet to implement your algorithm. Begin coding your algorithm. Run the test again: if it fails, revise the code. Once the code passes the test, move onto the next algorithm. Test driven development (TDD)Test driven development (TDD)
- 4. Consider what the function should do Consider what inputs are required Consider what outputs are expected Planning your testPlanning your test
- 5. 1.import unittest 2.from Task_1 import * 3.class TestSequenceFunctions(unittest.TestCase): 4. deftest_rollDie(self): 5. maxNumOfSides= 20 6. for die in range(2, maxNumOfSides+1): 7. value = rollDie(die) 8. self.assertTrue(value >= 1) 9. self.assertTrue(value <= maxNumOfSides) Example Python unit testExample Python unit test
- 6. Steps to create a unit testSteps to create a unit test Create a new .pyfile for your test code Import the Python unittest library –import unittest Import your code to test –from <file name> import * Create a class for your tests by inheriting from unittest.TestCase –class <class name> (unittest.TestCase): Add one or more new test methods –def<method name>(self): Add assert statements to your method to notify the system of an error –self.assertEqual(<variable 1> , <variable 2>) –self.assertTrue(<condition> ) Run the test
- 7. assert methods available inassert methods available in Python’s unittest modulePython’s unittest module Method Checks that assertEqual(a,b) a == b assertNotEqual(a,b) a != b assertTrue(x) bool(x) is True assertFalse(x) bool(x) is False assertIn(a,b) a in b assertNotIn(a,b) a not in b If an assert method returns false, an error is logged and displayed in PyCharm Documentation: https://docs.python.org/3/library/unittest.html