Searching for Optimal Models: Comparing Two Encoding Approaches.

Abstract

Search-Based Software Engineering (SBSE) is about solving software development problems by formulating them as optimization problems. In the last years, combining SBSE and Model-Driven Engineering (MDE), where models and model transformations are treated as key artifacts in the development of complex systems, has become increasingly popular. While search-based techniques have often successfully been applied to tackle MDE problems, a recent line of research investigates how a model-driven design can make optimization more easily accessible to a wider audience. In previous model-driven optimization efforts, a major design decision concerns the way in which solutions are encoded. Two main options have been explored: a model-based encoding representing candidate solutions as models, and a rule-based encoding representing them as sequences of transformation rule applications. While both encodings have been applied to different use cases, no study has yet compared them systematically. To close this gap, we evaluate both approaches on a common set of optimization problems, investigating their impact on the optimization performance. Additionally, we discuss their differences, strengths, and weaknesses laying the foundation for a knowledgeable choice of the right encoding for the right problem.

Automatic generation of atomic multiplicity-preserving search operators for search-based model engineering

“…Such a comparison isn't useful in this paper, as the focus is comparing the effectiveness and efficiency of the Man and Gen operators. In [26], we compare the performance of MDEOptimiser and MOMoT, another model search tool that encodes search solutions as transformation chains.…”

“…1 MDEOptimiser approach general overview [26] Fig. 2 MDEOptimiser new candidate generation overview [26] Algorithm 1 Abstract Mutation Only EA return Population 16: end procedure Evolutionary Search Parameter Control When using EAs, a key challenge arises from the need to configure ideal algorithm parameters, both, offline, before the start of the search and, online during the search [2]. The parameters used at the start and during execution can help steer the search towards having a greater chance of finding near-optimal solutions.…”

“…Compared to MDEOptimiser, ViatraDSE and MOMoT do not offer any mutation operator generation, and users are required to provide model transformations to be used for search space exploration. John et al [26] evaluate the performance of model-based and rule-based search by comparing MDEOptimiser and MOMoT.…”

“…Evolutionary algorithms have been applied to MDE in multiple ways [8,26]: some approaches (e.g. [1,18]) encode candidate solutions as sequences of transformation rules applications and apply genetic algorithms to solve the search problems.…”

Towards Scalable Search-Based Model Engineering with MDEOptimiser Scale

“…The case study consists of five input models, however, because of space limitations, for this demonstration, only input models A and B will be used. A complete evaluation of this case study using the two tools used in this configuration can be found in [4].…”