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We propose to integrate ACO in a Constraint Programming (CP) language. Basically, we use the CP language to describe the problem to solve by means of constraints and we use the CP propagation engine to reduce the search space and check constraint satisfaction; however, the classical backtrack search of CP is replaced by an ACO search. We report first experimental results on the car sequencing problem and compare different pheromone strategies for this problem.

Context: Most companies, independently of their size and activity type, are facing the problem of managing, maintaining and/or replacing (part of) their existing software systems. These legacy systems are often large applications playing a critical role in the company's information system and with a non-negligible impact on its daily operations. Improving their comprehension (e.g., architecture, features, enforced rules, handled data) is a key point when dealing with their evolution/modernization.Objective: The process of obtaining useful higher-level representations of (legacy) systems is called reverse engineering (RE), and remains a complex goal to achieve. Socalled Model Driven Reverse Engineering (MDRE) has been proposed to enhance more traditional RE processes. However, generic and extensible MDRE solutions potentially addressing several kinds of scenarios relying on different legacy technologies are still missing or incomplete. This paper proposes to make a step in this direction.Method: MDRE is the application of Model Driven Engineering (MDE) principles and techniques to RE in order to generate relevant model-based views on legacy systems, thus facilitating their understanding and manipulation. In this context, MDRE is practically used in order to 1) discover initial models from the legacy artifacts composing a given system and 2) understand (process) these models to generate relevant views (i.e., derived models) on this system. Results: Capitalizing on the different MDRE practices and our previous experience (e.g., in real modernization projects), this paper introduces and details the MoDisco open source MDRE framework. It also presents the underlying MDRE global methodology and architecture accompanying this proposed tooling.Conclusion: MoDisco is intended to make easier the design and building of modelbased solutions dedicated to legacy systems RE. As an empirical evidence of its relevance and usability, we report on its successful application in real industrial projects and on the concrete experience we gained from that.

Abstract-Organizations rely on the logic embedded in their Information Systems for their daily operations. This logic implements the business rules in place in the organization, which must be continuously adapted in response to market changes. Unfortunately, this evolution implies understanding and evolving also the underlying software components enforcing those rules. This is challenging because, first, the code implementing the rules is scattered throughout the whole system and, second, most of the time documentation is poor and out-of-date. This is specially true for older systems that have been maintained and evolved for several years (even decades). In those systems, it is not even clear which business rules are enforced nor whether rules are still consistent with the current organizational policies.In this sense, the goal of this paper is to facilitate the comprehension of legacy systems (in particular COBOL-based ones) by providing a model driven reverse engineering framework able to extract and visualize the business logic embedded in them.

We introduce two reactive frameworks for dynamically adapting some parameters of an Ant Colony Optimization (ACO) algorithm. Both reactive frameworks use ACO to adapt parameters: pheromone trails are associated with parameter values; these pheromone trails represent the learnt desirability of using parameter values and are used to dynamically set parameters in a probabilistic way. The two frameworks differ in the granularity of parameter learning. We experimentally evaluate these two frameworks on an ACO algorithm for solving constraint satisfaction problems.

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