Link to paper

We present a novel multi-parent crossover operator in genetic algorithms (GAs) called "Deep Neural Crossover" (DNC). Unlike conventional GA crossover operators that rely on a random selection of parental genes, DNC leverages the capabilities of deep reinforcement learning (DRL) and an encoder-decoder architecture to select the genes. Specifically, we use DRL to learn a policy for selecting promising genes. The policy is stochastic, to maintain the stochastic nature of GAs, representing a distribution for selecting genes with a higher probability of improving fitness. Our architecture features a recurrent neural network (RNN) to encode the parental genomes into latent memory states, and a decoder RNN that utilizes an attention-based pointing mechanism to generate a distribution over the next selected gene in the offspring. The operator's architecture is designed to find linear and nonlinear correlations between genes and translate them to gene selection. To reduce computational cost, we present a transfer-learning approach, wherein the architecture is initially trained on a single problem within a specific domain and then applied to solving other problems of the same domain. We compare DNC to known operators from the literature over two benchmark domains, outperforming all baselines.

A recording of a lecture that I gave at the international colloquium: "AI meets Software Quality."

ABSTRACT:

This talk will present ongoing work on creating a synergy between artificial intelligence and software engineering. Deep Learning (DL) is dramatically changing the world of software, enabling engineers to train models that can achieve super-human results. Yet, DL can err when faced with new situations, which is highly troubling if DL is to be used in critical systems. An exciting new approach augments DL-based systems with hand-crafted code. This approach allows leveraging decades of work in software engineering (SE).

Apart from the reliability of AI systems, recent AI advancements have started changing the way we develop software. AI is already used for code completion (i.e., GitHub Copilot), test generation, fault localization, weaknesses identification, and more. In many cases, these automated tools rely on big data and NLP and do not utilize the full power of SE.

We propose to bridge the gaps between the two fields by using Behavioral Programming (BP) at the core of this synergy. BP is a software development and modeling paradigm designed to allow users to program reactive systems in a natural and intuitive manner that is aligned with how humans perceive the system requirements. BP provides intuitive means for bridging the two fields.

Link to paper

We identify and demonstrate a weakness of Petri Nets (PN) in specifying composite behavior of reactive systems. Specifically, we show how, when specifying multiple requirements in one PN model, modelers are obliged to specify mechanisms for combining these requirements. This yields, in many cases, over-specification and incorrect models. We demonstrate how some execution paths are missed, and some are generated unintentionally. To support this claim, we analyze PN models from the literature, identify the combination mechanisms, and demonstrate their effect on the correctness of the model. To address this problem, we propose to model the system behavior using behavioral programming (BP), a software development and modeling paradigm designed for seamless integration of independent requirements. Specifically, we demonstrate how the semantics of BP, which define how to interweave scenarios into a single model, allow for avoiding the over-specification. Additionally, while BP maintains the same mathematical properties as PN, it provides means for changing the model dynamically, thus increasing the agility of the specification. We compare BP and PN in quantitative and qualitative measures by analyzing the models, their generated execution paths, and the specification process. Finally, while BP is supported by tools that allow for applying formal methods and reasoning techniques to the model, it lacks the legacy of PN tools and algorithms. To address this issue, we propose semantics and a tool for translating BP models to PN and vice versa.

Link to paper

Context:

Modern systems require programmers to develop code that dynamically adapts to different contexts, leading to the evolution of new context-oriented programming languages. These languages introduce new software-engineering challenges, such as: how to maintain the separation of concerns of the codebase? how to model the changing behaviors? how to verify the system behavior? and more.

Objective:

This paper introduces Context-Oriented Behavioral Programming (COBP) — a novel paradigm for developing context-aware systems, centered on natural and incremental specification of context-dependent behaviors. As the name suggests, we combine behavioral-programming (BP) — a scenario-based modeling paradigm — with context idioms that explicitly specify when scenarios are relevant and what information they need. The core idea is to connect the behavioral model with a data model that represents the context, allowing an intuitive connection between the models via update and select queries. Combining behavioral-programming with context-oriented programming brings the best of the two worlds, solving issues that arise when using each of the approaches in separation.

Methods:

We begin with providing abstract semantics for COBP and two implementations for the semantics, laying the foundations for applying reasoning algorithms to context-aware behavioral programs. Next, we exemplify the semantics with formal specifications of systems, including a variant of Conway’s Game of Life. Then, we provide two case studies of real-life context-aware systems (one in robotics and another in IoT) that were developed using this tool. Throughout the examples and case studies, we provide design patterns and a methodology for coping with the above challenges.

Results:

The case studies show that the proposed approach is applicable for developing real-life systems, and presents measurable advantages over the alternatives — behavioral programming alone and context-oriented programming alone.

Conclusion:

We present a paradigm allowing programmers and system engineers to capture complex context-dependent requirements and align their code with such requirements.