Learning to adapt requirements specifications of evolving systems (NIER track)

Borges, Rafael V.; Garcez, Artur d’Avila; Lamb, Luís C.; Nuseibeh, Bashar

doi:10.1145/1985793.1985924

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Learning Parameters. The problem of learning (requirement) parameters from simulations was extensively studied in the literature [96]- [98]. Our work is significantly different from those, since it aims to learn assumptions on the input signals.…”

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.

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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.

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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.

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“…Learning Parameters. Approaches that learn (requirement) parameters from simulations have been presented in the literature [21,42,43]. In contrast to those approaches, our work is explicitly tailored to learning assumptions.…”

Section: Related Workmentioning

confidence: 99%

Mining assumptions for software components using machine learning

Gaaloul

Menghi

Nejati

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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Software verification approaches aim to check a software component under analysis for all possible environments. In reality, however, components are expected to operate within a larger system and are required to satisfy their requirements only when their inputs are constrained by environment assumptions. In this paper, we propose EPIcuRus, an approach to automatically synthesize environment assumptions for a component under analysis (i.e., conditions on the component inputs under which the component is guaranteed to satisfy its requirements). EPIcuRus combines search-based testing, machine learning and model checking. The core of EPIcuRus is a decision tree algorithm that infers environment assumptions from a set of test results including test cases and their verdicts. The test cases are generated using search-based testing, and the assumptions inferred by decision trees are validated through model checking. In order to improve the efficiency and effectiveness of the assumption generation process, we propose a novel test case generation technique, namely Important Features Boundary Test (IFBT), that guides the test generation based on the feedback produced by machine learning. We evaluated EPIcuRus by assessing its effectiveness in computing assumptions on a set of study subjects that include 18 requirements of four industrial models. We show that, for each of the 18 requirements, EPIcuRus was able to compute an assumption to ensure the satisfaction of that requirement, and further, ≈ 78% of these assumptions were computed in one hour.

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“…2 Although it could become relevant with long-running systems, if, for instance, the robot's battery becomes progressively unreliable.…”

Section: B Probability Estimationmentioning

confidence: 99%

“…Our approach moves the process to runtime, using feedback from the running system as counter-examples (traces that violate the goal). The work in [2] likewise uses machine learning to refine specifications, in this case using neuralsymbolic networks [17] that handle noise. In addition feedback from the running system is used to learn specifications.…”

Section: Related Workmentioning

confidence: 99%

Learning revised models for planning in adaptive systems

Sykes

Corapi

Magee

et al. 2013

2013 35th International Conference on Software Engineering (ICSE)

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Environment domain models are a key part of the information used by adaptive systems to determine their behaviour. These models can be incomplete or inaccurate. In addition, since adaptive systems generally operate in environments which are subject to change, these models are often also out of date. To update and correct these models, the system should observe how the environment responds to its actions, and compare these responses to those predicted by the model. In this paper, we use a probabilistic rule learning approach, NoMPRoL, to update models using feedback from the running system in the form of execution traces. NoMPRoL is a technique for nonmonotonic probabilistic rule learning based on a transformation of an inductive logic programming task into an equivalent abductive one. In essence, it exploits consistent observations by finding general rules which explain observations in terms of the conditions under which they occur. The updated models are then used to generate new behaviour with a greater chance of success in the actual environment encountered.

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Learning to adapt requirements specifications of evolving systems (NIER track)

Cited by 6 publications

References 9 publications

Combining Genetic Programming and Model Checking to Generate Environment Assumptions

Combining Genetic Programming and Model Checking to Generate Environment Assumptions

Mining assumptions for software components using machine learning

Learning revised models for planning in adaptive systems

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