Robust Software Labs
software robustness assessment • error prevention • test driven development • machine learning system testing • dataset analysis
We pride ourselves on being a leading provider of software analysis, improvement, development, and other R&D services that ensure exceptional software quality management throughout the entire application life cycle. Our professional competence and extensive practical experience enables us to deliver timely and targeted solutions to complex challenges. Proprietary knowledge and in-house tooling is at the core of our approach, allowing us to innovate while executing targeted solutions to particular challenges.Our software projects always incorporate best practices, resulting in both quality and efficiency. With our performance-oriented approach, backed by extensive experience, we execute for results while taking economic conditions into account.
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Robust Software Labs OÜ
Registry Code: 16661850
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What is robust software?
"Robust software" is a term used to describe software that is reliable, resilient, and able to handle unexpected conditions without crashing or producing incorrect results. It is also a software that is able to maintain its functionality despite changes in the operating environment or new requirements. This can refer to software that is used in a wide range of different settings, from consumer products to enterprise systems, and from mobile applications to embedded systems.In order for software to be considered robust, it must be able to handle a wide range of inputs and conditions without breaking or producing incorrect results. This can include handling invalid input, unexpected usage patterns, or changes in the operating environment. Additionally, robust software should be able to handle errors and exceptions gracefully, without crashing or leaving the system in an unstable state.Another important aspect of robust software is its ability to adapt to changes. This can include changes in requirements, changes in the underlying hardware or operating system, or changes in the environment in which the software is being used. A robust software should be able to handle these changes without requiring extensive modifications to the codebase, which makes it more maintainable and cost-effective over time.Robust software also tends to be more secure. Because it's been built to handle unexpected inputs and conditions, it is less likely to contain vulnerabilities that could be exploited by attackers. Additionally, robust software tends to use resources efficiently, which can help to reduce costs for both the company and the end-users. All these features make robust software more reliable and usable, which in turn help to increase user satisfaction and build a positive reputation for the company.
Robustness for machine learning systems?
Robustness is particularly important for systems based on machine learning (ML) because these systems often operate in unpredictable environments and must be able to handle unexpected inputs or conditions without crashing or producing incorrect results. For example, a self-driving car must be able to handle a wide range of sensor inputs and make safe decisions even in unexpected situations, while a natural language processing system must be able to understand and respond to input that deviates from the norm.One way to make ML systems more robust is to use techniques from robust optimization and adversarial learning, which help to make the system's decision-making process more robust to small changes in the input data. Additionally, techniques such as ensemble learning and data augmentation can also help to make the system more robust by reducing the impact of any single training sample or model.Another important aspect of robustness for ML systems is the quality of the training data. To be robust, ML systems must be trained on high-quality and diverse datasets that adequately represent the problem domain and the range of inputs and conditions the system will encounter in real-world scenarios. Additionally, robust ML models should be able to generalize well, meaning that they can make accurate predictions even when presented with inputs that are not seen in the training dataset.Lastly, robustness also puts requirements on the evaluation process, robust models should be tested on diverse datasets to check for good performance in different scenarios and also tested for their adversarial robustness to ensure that the system can't be easily manipulated by malicious inputs. In general, robust ML systems should be designed and tested with a consideration for the real-world scenarios in which they will be used, taking into account the range of possible inputs, conditions, and error modes.