Search NASA⌕ Search

SEARCH · Search NASA

Results for “test frameworks”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

IBPSA Project 2 BOPTEST: An update on the test cases available in the framework for testing advanced control strategies in buildings

Project 2 develops software infrastructure, test cases, and extensions for the Building Optimization Testing Framework (BOPTEST) to address the expanding needs of building and urban energy system controls through open international collaboration. This paper provides an overview of the new test cases available as of BOPTEST version 0.7.1. Each test case is developed using open-source Modelica libraries and Spawn of EnergyPlus, enabling the creation of high-fidelity building models that incorporate envelope dynamics, Heating Ventilation and Air Conditioning (HVAC) systems, and explicit control representations. Currently, eight test cases are available, with five additional cases under development. These test cases cover a wide range of climates, building types, and HVAC systems. This paper compiles and summarizes test case descriptions, cites original manuscripts that developed them for a more detailed description, and reports baseline control performance metrics. Furthermore, two example applications are presented: one illustrating different levels of control, from supervisory to low-level, and another demonstrating how Model Predictive Control (MPC) solutions must be adapted from continuous to integer to control some building actuators.

Zanetti, Ettore↗
NASA NTRS2013

Adoption of Test Driven Development and Continuous Integration for the Development of the Trick Simulation Toolkit

This paper describes the adoption of a Test Driven Development approach and a Continuous Integration System in the development of the Trick Simulation Toolkit, a generic simulation development environment for creating high fidelity training and engineering simulations at the NASA/Johnson Space Center and many other NASA facilities. It describes what was learned and the significant benefits seen, such as fast, thorough, and clear test feedback every time code is checked-in to the code repository. It also describes a system that encourages development of code that is much more flexible, maintainable, and reliable. The Trick Simulation Toolkit development environment provides a common architecture for user-defined simulations. Trick builds executable simulations using user-supplied simulation-definition files (S_define) and user supplied "model code". For each Trick-based simulation, Trick automatically provides job scheduling, checkpoint / restore, data-recording, interactive variable manipulation (variable server), and an input-processor. Also included are tools for plotting recorded data and various other supporting tools and libraries. Trick is written in C/C++ and Java and supports both Linux and MacOSX. Prior to adopting this new development approach, Trick testing consisted primarily of running a few large simulations, with the hope that their complexity and scale would exercise most of Trick's code and expose any recently introduced bugs. Unsurprising, this approach yielded inconsistent results. It was obvious that a more systematic, thorough approach was required. After seeing examples of some Java-based projects that used the JUnit test framework, similar test frameworks for C and C++ were sought. Several were found, all clearly inspired by JUnit. Googletest, a freely available Open source testing framework, was selected as the most appropriate and capable. The new approach was implemented while rewriting the Trick memory management component, to eliminate a fundamental design flaw. The benefits became obvious almost immediately, not just in the correctness of the individual functions and classes but also in the correctness and flexibility being added to the overall design. Creating code to be testable, and testing as it was created resulted not only in better working code, but also in better-organized, flexible, and readable (i.e., articulate) code. This was, in essence the Test-driven development (TDD) methodology created by Kent Beck. Seeing the benefits of Test Driven Development, other Trick components were refactored to make them more testable and tests were designed and implemented for them.

Penn, John M.↗
DOE OSTI2018

Test Harness Framework

The code facilitates running tests and then validating the results. The tests are normally attacks on a system and the verification is normally to validate protection software detected the attack, however the framework can be used in any area.

McCarty, MichaelV↗
NASA NTRS2009

Exploring the Use of a Test Automation Framework

It is known that software testers, more often than not, lack the time needed to fully test the delivered software product within the time period allotted to them. When problems in the implementation phase of a development project occur, it normally causes the software delivery date to slide. As a result, testers either need to work longer hours, or supplementary resources need to be added to the test team in order to meet aggressive test deadlines. One solution to this problem is to provide testers with a test automation framework to facilitate the development of automated test solutions.

TAF STAF Test Automation↗
NASA NTRS2019

Testing Scientific Software: Challenges and Remedies

Over the past two decades, the emergence of highly effective software testing frameworks has greatly simplified the development and use of unit tests and has led to new software development paradigms such as test driven development (TDD). However, scientific computing introduces a number of unique testing challenges, including numerical algorithms, distributed parallelism, and exascale environments. This presentation will begin with a brief introduction to unit testing, testing frameworks, and some simple examples using pFUnit, a unit testing framework for Fortran + MPI. I will then take a closer look at several of the obstacles one faces when testing technical software and suggest methodologies that can mitigate these difficulties.

Clune, Tom↗
NASA NTRS2009

(Quickly) Testing the Tester via Path Coverage

The configuration complexity and code size of an automated testing framework may grow to a point that the tester itself becomes a significant software artifact, prone to poor configuration and implementation errors. Unfortunately, testing the tester by using old versions of the software under test (SUT) may be impractical or impossible: test framework changes may have been motivated by interface changes in the tested system, or fault detection may become too expensive in terms of computing time to justify running until errors are detected on older versions of the software. We propose the use of path coverage measures as a "quick and dirty" method for detecting many faults in complex test frameworks. We also note the possibility of using techniques developed to diversify state-space searches in model checking to diversify test focus, and an associated classification of tester changes into focus-changing and non-focus-changing modifications.

evaluation of test systems↗
NASA NTRS2019

Testing Fortran Software with pFunit

Over the past two decades, the emergence of highly effective software testing frameworks has greatly simplified the development and use of unit tests and has led to new software development paradigms such as test driven development (TDD). However, technical computing introduces a number of unique testing challenges, including distributed parallelism and numerical accuracy. This webinar will begin with a basic introduction to the use of pFUnit (parallel Fortran Unit testing framework) to develop tests for Message Passing Interface (MPI) plus Fortran (MPI+Fortran) software and then present some of the new capabilities in the latest release. We will also discuss some specialized methodologies for testing numerical algorithms and speculate about future framework capabilities that may improve our ability to test at exascale.

Clune, Tom↗
DOE OSTI2019

Parallel Computing for Module-Based Computational Experiment

Large-scale scientific code plays an important role in scientific researches. In order to facilitate module and element evaluation in scientific applications, we introduce a unit testing framework and describe the demand for module-based experiment customization. We then develop a parallel version of the unit testing framework to handle long-term simulations with a large amount of data. Specifically, we apply message passing based parallelization and I/O behavior optimization to improve the performance of the unit testing framework and use profiling result to guide the parallel process implementation. Finally, we present a case study on litter decomposition experiment using a standalone module from a large-scale Earth System Model. This case study is also a good demonstration on the scalability, portability, and high-efficiency of the framework.

Yao, Zhuo↗