Changing Software in a Changing World: How to Test in Presence of Variability, Adaptation and Evolution?

Bertolino, Antonia; Inverardi, Paola

doi:10.1007/978-3-030-30985-5_5

Cited by 9 publications

(5 citation statements)

References 35 publications

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“…Testing SAS at run time is used to provide assurance that the system will behave as designed [14]. is is particularly di cult for SAS because of the uncertainties about the impacts of system recon gurations or environment changes [23], which are all inherent to the unanticipated operational scenarios [5]. Hence, solutions for testing SAS focus on mitigating di erent types of uncertainty in the models of the system and the environment.…”

Section: Testing Self-adaptive Systems -Sasmentioning

confidence: 99%

“…Ultimately, if we compare with the design of SAS, research on testing these systems is still lacking [5,54]. As pointed out by Chechik et al [9], safety standards like DO-178C (aerospace) and ISO 262622 (automotive) provide recommendations on testing.…”

Section: Testing Self-driving Carsmentioning

confidence: 99%

“…e engineering challenge is how to provide safety-critical assurances when the operational scenario changes [5,9]. e approach has been to design systems with self-adaptation capabilities [14], for instance, feedback control-loops [11] and runtime models [58].…”

Section: Introductionmentioning

confidence: 99%

“…However, the current state of the art still lacks in terms of guidance to allocate tests when the test evidence is partial, and the input data is sparse. ese are characteristic of the unanticipated scenarios faced by a SAS operating in a changing environment [5,9] and they stem from the fact that tests cannot guarantee the absence of failures [16] because there is no number of tests that can uncover all defects in a so ware.…”

Section: Introductionmentioning

confidence: 99%

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Collective Risk Minimization via a Bayesian Model for Statistical Software Testing

Haensel¹,

Adriano²,

Dyck³

et al. 2020

Preprint

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In the last four years, the number of distinct autonomous vehicles platforms deployed in the streets of California increased 6-fold, while the reported accidents increased 12-fold. is can become a trend with no signs of subsiding as it is fueled by a constant stream of innovations in hardware sensors and machine learning so ware. Meanwhile, if we expect the public and regulators to trust the autonomous vehicle platforms, we need to nd be er ways to solve the problem of adding technological complexity without increasing the risk of accidents. We studied this problem from the perspective of reliability engineering in which a given risk of an accident has severity and probability of occurring. Timely information on accidents is important for engineers to anticipate and reuse previous failures to approximate the risk of accidents in a new city. However, this is challenging in the context of autonomous vehicles because of the sparse nature of data on the operational scenarios (driving trajectories in a new city). Our approach was to mitigate data sparsity by reducing the state space through monitoring of multiple-vehicles operations. We then minimized the risk of accidents by determining proper allocation of tests for each equivalence class. Our contributions comprise (1) a set of strategies to monitor the operational data of multiple autonomous vehicles, (2) a Bayesian model that estimates changes in the risk of accidents, and (3) a feedback control-loop that minimizes these risks by reallocating test e ort. Our results are promising in the sense that we were able to measure and control risk for a diversity of changes in the operational scenarios. We evaluated our models with data from two real cities with distinct tra c pa erns and made the data available for the community.

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Section: Testing Self-adaptive Systems -Sasmentioning

confidence: 99%

Section: Testing Self-driving Carsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Collective Risk Minimization via a Bayesian Model for Statistical Software Testing

Haensel¹,

Adriano²,

Dyck³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In general, testing is a crucial aspect of software development. For self-adaptive software, the testing process is complicated by the presence of the adaptation layer [7,10,33,62]. Self-adaptive systems testing is intrinsically hard, due to the extreme variability and uncertainty involved in the software execution [5,8,20,62].…”

Section: Introductionmentioning

confidence: 99%

Testing self-adaptive software with probabilistic guarantees on performance metrics

Mandrioli

Maggio

2020

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

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A survey on runtime testing of dynamically adaptable and distributed systems

Lahami

Krichen

2021

Software Qual J

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Changing Software in a Changing World: How to Test in Presence of Variability, Adaptation and Evolution?

Cited by 9 publications

References 35 publications

Collective Risk Minimization via a Bayesian Model for Statistical Software Testing

Collective Risk Minimization via a Bayesian Model for Statistical Software Testing

Testing self-adaptive software with probabilistic guarantees on performance metrics

A survey on runtime testing of dynamically adaptable and distributed systems

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