Learning Effective Changes for Software Projects

The primary motivation of much of software analytics is decision making. How to make these decisions? Should one make decisions based on lessons that arise from within a particular project? Or should one generate these decisions from across multiple projects? This work is an attempt to answer these questions. Our work was motivated by a realization that much of the current generation software analytics tools focus primarily on prediction. Indeed prediction is a useful task, but it is usually followed by "planning" about what actions need to be taken. This research seeks to address the planning task by seeking methods that support actionable analytics that offer clear guidance on what to do. Specifically, we propose XTREE and BELLTREE algorithms for generating a set of actionable plans within and across projects. Each of these plans, if followed will improve the quality of the software project.Comment: 4 pages, 2 figures. This a submission for ASE 2017 Doctoral Symposiu

An ontology-based approach to relax traffic regulation for autonomous vehicle assistance

Traffic regulation must be respected by all vehicles, either human- or computer- driven. However, extreme traffic situations might exhibit practical cases in which a vehicle should safely and reasonably relax traffic regulation, e.g., in order not to be indefinitely blocked and to keep circulating. In this paper, we propose a high-level representation of an automated vehicle, other vehicles and their environment, which can assist drivers in taking such "illegal" but practical relaxation decisions. This high-level representation (an ontology) includes topological knowledge and inference rules, in order to compute the next high-level motion an automated vehicle should take, as assistance to a driver. Results on practical cases are presented

Planning and Scheduling of Business Processes in Run-Time: A Repair Planning Example

Over the last decade, the efficient and flexible management of business processes has become one of the most critical success aspects. Furthermore, there exists a growing interest in the application of Artificial Intelligence Planning and Scheduling techniques to automate the production and execution of models of organization. However, from our point of view, several connections between both disciplines remains to be exploited. The current work presents a proposal for modelling and enacting business processes that involve the selection and order of the activities to be executed (planning), besides the resource allocation (scheduling), considering the optimization of several functions and the reach of some objectives. The main novelty is that all decisions (even the activities selection) are taken in run-time considering the actual parameters of the execution, so the business process is managed in an efficient and flexible way. As an example, a complex and representative problem, the repair planning problem, is managed through the proposed approach.Ministerio de Ciencia e Innovación TIN2009-13714Junta de Andalucía P08-TIC-0409

Trajectory Planning on Grids: Considering Speed Limit Constraints

Trajectory (path) planning is a well known and thoroughly studied field of automated planning. It is usually used in computer games, robotics or autonomous agent simulations. Grids are often used for regular discretization of continuous space. Many methods exist for trajectory (path) planning on grids, we address the well known A* algorithm and the state-of-the-art Theta* algorithm. Theta* algorithm, as opposed to A*, provides ‘any-angle‘ paths that look more realistic. In this paper, we provide an extension of both these algorithms to enable support for speed limit constraints.We experimentally evaluate and thoroughly discuss how the extensions affect the planning process showing reasonability and justification of our approach

OptBPPlanner: Automatic Generation of Optimized Business Process Enactment Plans

Unlike imperative models, the specifi cation of business process (BP) properties in a declarative way allows the user to specify what has to be done instead of having to specify how it has to be done, thereby facilitating the human work involved, avoiding failures, and obtaining a better optimization. Frequently, there are several enactment plans related to a specifi c declarative model, each one presenting specifi c values for different objective functions, e.g., overall completion time. As a major contribution of this work, we propose a method for the automatic generation of optimized BP enactment plans from declarative specifi cations. The proposed method is based on a constraint-based approach for planning and scheduling the BP activities. These optimized plans can then be used for different purposes like simulation, time prediction, recommendations, and generation of optimized BP models. Moreover, a tool-supported method, called OptBPPlanner, has been implemented to demonstrate the feasibility of our approach. Furthermore, the proposed method is validated through a range of test models of varying complexity.Ministerio de Ciencia e Innovación TIN2009-1371