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Variability constraints are an integral part of the requirements for a configurable system. The constraints specified in the requirements on the legal combinations of options define the space of potential valid configurations for the system-to- be. This paper reports on our experience with the variability- related requirements constraints of a flight software framework used by multiple space missions. A challenge that we saw for practitioners using the current framework, now open-sourced, is that the specifications of its variability-related requirements and constraints are dispersed across several documents, rather than being centralized in the software requirements specification. Such dispersion can contribute to misunderstandings of the side-effects of design choices, increased effort for developers, and bugs during operations. Based on our experience, we propose a new software variability model, similar to a product-line feature model, in the flight software framework. We describe the structured technique by which our model is developed, demonstrate its use, and evaluate it on a key service module of the flight software. Results show that our lightweight modeling technique helped find missing and inconsistent variability-related requirements and constraints. More generally, we suggest that a variability modeling technique such as this can be an efficient way for developers to centralize the specification and improve the analysis of dispersed variability-related requirements and constraints in other configurable systems. Index Terms—Requirement analysis, Variability constraints, Variability requirements, Configurable system, Feature model
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Exploration of Failures in an sUAS Controller Software Product Line
Small uncrewed aerial systems (sUAS) are growing in their use for commercial, scientific, recreational, and emergency management purposes. A critical part of a successful flight is a correctly tuned controller which manages the physics of the vehicle. If improperly configured, it can lead to flight instability, deviation, or crashes. These types of misconfigurations are often within the valid ranges specified in the documentation; hence, they are hard to identify. Recent research has used fuzzing or explored only a small part of the parameter space, providing little understanding of the configuration landscape itself. In this work we leverage software product line engineering to model a subset of the parameter space of a widely used flight control software, using it to guide a systematic exploration of the controller space. Via simulation, we test over 20,000 configurations from a feature model with 50 features and 8.88 × 1034 products, covering all single parameter value changes and all pairs of changes from their default values. Our results show that only a small number of single configuration changes fail (15%), however almost 40% fail when we evaluate changes to two-parameters at a time. We explore the interactions between parameters in more detail, finding what appear to be many dependencies and interactions between parameters which are not well documented. We then explore a smaller, exhaustive product line model, with eight of the most important features (and 6,561 configurations) and uncover a complex set of interactions; over 48% of all configurations fail.
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- PAR ID:
- 10548898
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400705939
- Page Range / eLocation ID:
- 125 to 135
- Format(s):
- Medium: X
- Location:
- Dommeldange Luxembourg
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3646548.3672597
