Test case generation from natural language requirements based on SCR specifications

Carvalho, Gustavo; Falcão, Diogo; Barros, Flávia A.; Sampaio, Augusto; Mota, Alexandre; Motta, Leonardo; Blackburn, Mark

doi:10.1145/2480362.2480591

Cited by 22 publications

(19 citation statements)

References 12 publications

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“…Once the CSP specifications have been generated, the model checker FDR [15] can check the traces from the refinement property which then serve as test scenarios. In earlier work, DFRS had been transformed into other formalisms such as software cost reduction (SCR) [16], internal model representation (IMR) [17], and coloured Petri nets [18]. Independently of the formal approach used, the intention was always to generate test cases automatically.…”

Section: Background and Related Workmentioning

confidence: 99%

Formal Verification of CNL Health Recommendations

Rahman

Bowles

2017

Lecture Notes in Computer Science

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Abstract. Clinical texts, such as therapy algorithms, are often described in natural language and may include hidden inconsistencies, gaps and potential deadlocks. In this paper, we propose an approach to identify such problems with formal verification. From each sentence in the therapy algorithm we automatically generate a parse tree and derive case frames. From the case frames we construct a state-based representation (in our case a timed automaton) and use a model checker (here UPPAAL) to verify the model. Throughout the paper we use an example of the algorithm for blood glucose lowering therapy in adults with type 2 diabetes to illustrate our approach.

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Section: Background and Related Workmentioning

confidence: 99%

Formal Verification of CNL Health Recommendations

Rahman

Bowles

2017

Lecture Notes in Computer Science

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“…Concerning TC generation from natural language requirements, NAT2TEST [17] presents a strategy that maps requirements written in natural language into TCs using a formal notation for requirements specification (SCR) as an intermediate formalism. Another example is presented in [18], in which requirements must be written in a more restrictive way as a strict if-then sentence template.…”

Section: Doi Reference Number: 1018293/seke2016-228mentioning

confidence: 99%

Capture and Replay with Text-Based Reuse and Framework Agnosticism

Arruda

Sampaio

Barros

2016

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Software systems need to be constantly tested, either to verify changes or to check conformance to requirements. The current leading approaches to automate GUI tests are coding and the use of Capture & Replay (C&R) tools. Coding is usually associated with (even if ad hoc) reuse strategies, but requires from the developer specialized knowledge about the adopted framework. On the other hand, even though C&R is able to promote faster automation, it raises maintainability and scalability issues in the long term due to scripts scattering and rework for each new test case, because usually there is no associated reuse strategy. In order to combine the benefits of both approaches, we propose: an abstract and framework-free representation of test actions captured during testing activities; a text-based strategy that matches a new test case with previously recorded test actions; and a C&R tool that implements these concepts in the mobile context. We developed and evaluated our strategy in the context of a partnership with Motorola Mobility, achieving a reuse ratio up to 71% with time gains similar to traditional C&R approaches when compared to coding.

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“…In this paper, we both improve and extend an initial approach to test case generation from NL requirements using SCR [20]. The new contributions are:…”

Section: Contributionsmentioning

confidence: 99%

“…Finally, considering the preliminary version of this work [20], the improvements performed led us to better empirical results. Particularly, regarding the PC example, this improved version of our approach killed more mutants (95.52%) than the previous one (84%).…”

Section: Contributionsmentioning

confidence: 99%