Publications

Mining software repositories for patternizing attack-and-defense co-evolution
Several evidence indicates that malicious cyber actors learn, adapt, or, in other words, react to the defensive measures put into place …
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Visualization of aggregated information to support class-level software evolution

Context: Software is inherently prone to constant change, especially in the source code, making it difficult for developers to keep …

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B-AIS: An Automated Process for Black-box Evaluation of Visual Perception in AI-enabled Software against Domain Semantics

AI-enabled software systems (AIS) are prevalent in a wide range of applications, such as visual tasks of autonomous systems, …

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CADE: The Missing Benchmark in Evaluating Dataset Requirements of AI-enabled Software
The inductive nature of artificial neural models makes dataset quality a key factor of their proper functionality. For this reason, …
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A multi-level semantic web for hard-to-specify domain concept, Pedestrian, in ML-based software
Machine Learning (ML) algorithms are widely used in building software-intensive systems, including safety-critical ones. Unlike …
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Leveraging code-test co-evolution patterns for automated test case recommendation

Context: Prior research revealed that code components with similar structures tend to require structurally similar test cases and they …

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Patterns of Code-to-Test Co-evolution for Automated Test Suite Maintenance
Software systems are characterized by continual change which often occurs concurrently across various artifact types. While prior work …
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Towards requirements specification for machine-learned perception based on human performance
Software systems are characterized by continual change which often occurs concurrently across various artifact types. While prior work …
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Uncertainty, modeling and safety assurance: towards a unified framework
Uncertainty occurs naturally in software systems, including those that are model-based. When such systems are safety-critical, they …
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Toward requirements specification for machine-learned components
In current practice, the behavior of Machine-Learned Components (MLCs) is not sufficiently specified by the predefined requirements. …
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Leveraging Artifact Trees to Evolve and Reuse Safety Cases
Safety Assurance Cases (SACs) are increasingly used to guide and evaluate the safety of software-intensive systems. They are used to …
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Software assurance in an uncertain world
From financial services platforms to social networks to vehicle control, software has come to mediate many activities of daily life. …
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Evolving software trace links between requirements and source code
Traceability provides support for diverse software engineering activities including safety analysis, compliance verification, test-case …
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