A Tool for the Automatic Generation of Test Cases and Oracles for Simulation Models Based on Functional Requirements

Arrieta, Aitor; Agirre, Joseba A.; Sagardui, Goiuria

doi:10.1109/icstw50294.2020.00018

Cited by 8 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Algorithm optimization can also be combined with rule-based to adjust applicable rules (Sainani et al, 2020). Arrieta et al (2020) used Adaptive Random Testing (ART) to generate functional requirements in the test case. They combined with another tool to generate the functional requirement automatically.…”

Section: Optimization Algorithmmentioning

confidence: 99%

A scoping review of auto-generating transformation between software development artifacts

Siahaan,

Fauzan,

Widyadhana

et al. 2024

Front. Comput. Sci.

View full text Add to dashboard Cite

Every process within software development refers to a specific set of input and output artifacts. Each artifact models specific design information of a system, yet they complement each other and make an improved system description. The requirements phase is an early stage of software development that drives the rest of the development process. Throughout the software development life cycle, checking that every artifact produced in every development stage should comply with the given requirements is necessary. Moreover, there should be relatedness between elements within artifacts of different development stages. This study provides an overview of the conformity between artifacts and the possibility of artifact transformation. This study also describes the methods and tools used in previous studies for ensuring the conformity of artifacts with requirements in the transformation process between artifacts. It also provides their applications in the real world. The review identified three applications, seven methods and approaches, and five challenges in ensuring the conformity of artifacts with requirements. We identified the artifacts as class diagrams, aspect-oriented software architecture, architectural models, entity relationship diagrams, and sequence diagrams. The applications for ensuring the conformity of artifacts with requirements are maintaining traceability, software verification and validation, and software reuse. The methods include information retrieval, natural language processing, model transformations, text mining, graph-based, ontology-based, and optimization algorithms. The benefits of adopting methods and tools for ensuring the conformity of artifacts with requirements can motivate and assist practitioners in designing and creating artifacts.

show abstract

Section: Optimization Algorithmmentioning

confidence: 99%

A scoping review of auto-generating transformation between software development artifacts

Siahaan,

Fauzan,

Widyadhana

et al. 2024

Front. Comput. Sci.

View full text Add to dashboard Cite

show abstract

“…Compositional reasoning. Assume-guarantee and design by contract approaches were proposed in the literature to support hardware and software verification (e.g., [3]- [6], [71]- [76]). Assume-guarantee contracts represent the assumptions a CPS component makes about its environment, and the properties it satisfies when these assumptions hold, i.e., its guarantees.…”

Section: Threats To Validitymentioning

confidence: 99%

Combining Genetic Programming and Model Checking to Generate Environment Assumptions

Gaaloul

Menghi

Nejati

et al. 2022

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

Software verification may yield spurious failures when environment assumptions are not accounted for. Environment assumptions are the expectations that a system or a component makes about its operational environment and are often specified in terms of conditions over the inputs of that system or component. In this article, we propose an approach to automatically infer environment assumptions for Cyber-Physical Systems (CPS). Our approach improves the state-of-the-art in three different ways: First, we learn assumptions for complex CPS models involving signal and numeric variables; second, the learned assumptions include arithmetic expressions defined over multiple variables; third, we identify the trade-off between soundness and informativeness of environment assumptions and demonstrate the flexibility of our approach in prioritizing either of these criteria.We evaluate our approach using a public domain benchmark of CPS models from Lockheed Martin and a component of a satellite control system from LuxSpace, a satellite system provider. The results show that our approach outperforms stateof-the-art techniques on learning assumptions for CPS models, and further, when applied to our industrial CPS model, our approach is able to learn assumptions that are sufficiently close to the assumptions manually developed by engineers to be of practical value.

show abstract

“…Automated generation of the fitness function (e.g., [14,27,34,56,66,73,92]) derives the fitness function from other artifacts without any human intervention. For example, many SBST frameworks (e.g., [10,32,67,93]) use fitness functions to compute the robustness values derived from temporal logic specifications expressing system requirements (e.g., [34,41,68,73]).…”

Section: Introductionmentioning

confidence: 99%

“…These functions typically use the requirement structure to guide the search toward portions of the input domain that are more likely to lead to system failures. Automatically generated fitness functions support SBST in the detection of failures (e.g., [14,27,41,92]), were used to detect failures in industrial models (e.g., [47,67]), and are used within international tool competitions (e.g., [33]). They are general purpose.…”

Section: Introductionmentioning

confidence: 99%

Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions

Formica¹,

Askarpour²,

Menghi³

2022

Preprint

View full text Add to dashboard Cite

Search-based software testing (SBST) typically relies on fitness functions to guide the search exploration toward software failures. There are two main techniques to define fitness functions: (a) automated fitness function computation from the specification of the system requirements and (b) manual fitness function design. Both techniques have advantages. The former uses information from the system requirements to guide the search toward portions of the input domain that are more likely to contain failures. The latter uses the engineers' domain knowledge.We propose ATheNA, a novel SBST framework that combines fitness functions that are automatically generated from requirements specifications and manually defined by engineers. We design and implement ATheNA-S, an instance of ATheNA that targets Simulink ® models. We evaluate ATheNA-S by considering a large set of models and requirements from different domains. We compare our solution with an SBST baseline tool that supports automatically generated fitness functions, and another one that supports manually defined fitness functions. Our results show that ATheNA-S generates more failure-revealing test cases than the baseline tools and that the difference between the performance of ATheNA-S and the baseline tools is not statistically significant. We also assess whether ATheNA-S could generate failure-revealing test cases when applied to a large case study from the automotive domain. Our results show that ATheNA-S successfully revealed a requirement violation in our case study.

show abstract

A Tool for the Automatic Generation of Test Cases and Oracles for Simulation Models Based on Functional Requirements

Cited by 8 publications

References 18 publications

A scoping review of auto-generating transformation between software development artifacts

A scoping review of auto-generating transformation between software development artifacts

Combining Genetic Programming and Model Checking to Generate Environment Assumptions

Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions

Contact Info

Product

Resources

About