2014 Taverna tutorial Simple workflow
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This document provides a tutorial on building a simple workflow in Taverna to retrieve a protein sequence from a remote database and identify functional motifs. It describes connecting input and output ports, running the workflow by providing a protein identifier, and viewing the resulting sequence output. It also explains how to validate that the workflow connections are proper and that the services are available.
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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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Exceptional Behaviors: How Frequently Are They Tested? (AST 2025)
Exceptional Behaviors: How Frequently Are They Tested? (AST 2025)Andre Hora Exceptions allow developers to handle error cases expected to occur infrequently. Ideally, good test suites should test both normal and exceptional behaviors to catch more bugs and avoid regressions. While current research analyzes exceptions that propagate to tests, it does not explore other exceptions that do not reach the tests. In this paper, we provide an empirical study to explore how frequently exceptional behaviors are tested in real-world systems. We consider both exceptions that propagate to tests and the ones that do not reach the tests. For this purpose, we run an instrumented version of test suites, monitor their execution, and collect information about the exceptions raised at runtime. We analyze the test suites of 25 Python systems, covering 5,372 executed methods, 17.9M calls, and 1.4M raised exceptions. We find that 21.4% of the executed methods do raise exceptions at runtime. In methods that raise exceptions, on the median, 1 in 10 calls exercise exceptional behaviors. Close to 80% of the methods that raise exceptions do so infrequently, but about 20% raise exceptions more frequently. Finally, we provide implications for researchers and practitioners. We suggest developing novel tools to support exercising exceptional behaviors and refactoring expensive try/except blocks. We also call attention to the fact that exception-raising behaviors are not necessarily “abnormal” or rare.
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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.
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.
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- 1. Taverna Tutorial Building a simple workflow Stian Soiland-Reyes and Christian Brenninkmeijer University of Manchester materials by Katy Wolstencroft and Aleksandra Pawlik http://orcid.org/0000-0001-9842-9718 http://orcid.org/0000-0002-2937-7819 http://orcid.org/0000-0002-1279-5133 http://orcid.org/0000-0001-8418-6735 Bonn University, 2014-09-01 http://www.taverna.org.uk/ This work is licensed under a Creative Commons Attribution 3.0 Unported License
- 2. Available Service We will start with something easy - retrieving a protein sequence from a remote database and identifying functional motifs Expand the list Available Services Local Services ncbi Select ‘Get Protein FASTA’ and drag-and-drop it into the empty workflow diagram
- 3. Workflow input/output ports In a blank space in the workflow diagram, Right-click and select Workflow input port from the Insert section Type in a name for this input (e.g. ID) and click ok Do the same to create a new Workflow Output Port. Call this output Sequence
- 4. Connecting ports You now have 3 boxes in the diagram and we need to connect them up to build our workflow Click on the input box ID and drag towards Get_Protein_Fasta and let go. An arrow will connect the two boxes.
- 5. Your first workflow Click on the output box Sequence, drag towards “Get_protein_fasta”, and let go. An arrow will connect the two boxes. You have now built your first workflow!
- 6. Running workflow In the menu, select “File -> Run workflow”, or click on the green play button at the top of the workbench
- 7. Providing workflow inputs An input window will appear. We have not yet added a description of the workflow or the input. Click on ‘Set Value’ in the input window and add a Uniprot protein identifier (e.g. P15409) where it says “some input data goes here”
- 8. Workflow results Click Run workflow The workbench changes to the Results perspective In the bottom left, click on Value 1 You will now see a protein sequence from Uniprot
- 9. Validate your Workflow Taverna can check to see that everything is connected properly and that all the services in your workflow are available Go to the workflow explorer (“Design” button) and click on ‘Validation report’ tab See if Taverna has found any problems with the workflow. Errors will be displayed in red, warnings in yellow. Workflows with warnings often still run. If there are problems, follow the instructions to resolve them by clicking on the ‘Solution’ tab Are you able to create a workflow that gives warnings or errors? Tip: Try deleting the data link to the workflow output port