App Builder Feature Roundup and Roadmap
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Over the last few months, we have demonstrated a variety of components and capabilities within App Builder that can assist you in accelerating your next application development. Our next session in our accelerator series will take a closer look at some of newly added features from our last release, as well as discuss future plans and our App Builder roadmap.
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We often trust them with the safety of our codebase, but how do we know that we should? How do we know that this trust is well-deserved?
Enter mutation testing – by intentionally injecting harmful mutations into our code and seeing if they are caught by the tests, we can evaluate the quality of the safety net they provide. By watching the watchmen, we can make sure our tests really protect us, and we aren’t just green-washing our IDEs to a false sense of security.
Talk from SciFiDevCon 2025
https://www.scifidevcon.com/courses/2025-scifidevcon/contents/680efa43ae4f5
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
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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.
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TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...
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.
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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.
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Presented at: The 33rd International Conference on Program Comprehension (ICPC '25)
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Preprint: https://arxiv.org/abs/2501.10037
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