Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 23rd International Conference on Fundamental Approaches to Software Engineering, FASE 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 23 full papers, 1 tool paper and 6 testing competition papers presented in this volume were carefully reviewed and selected from 81 submissions. The papers cover topics such as requirements engineering, software architectures, specification, software quality, validation, verification of functional and non-functional properties, model-driven development and model transformation, software processes, security and software evolution

Robot planning based on boolean specifications using petri net models

In this paper, we propose an automated method for planning a team of mobile robots such that a Boolean-based mission is accomplished. The task consists of logical requirements over some regions of interest for the agents'' trajectories and for their final states. In other words, we allow combinatorial specifications defining desired final states whose attainment includes visits to, avoidance of, and ending in certain regions. The path planning approach should select such final states that optimize a certain global cost function. In particular, we consider minimum expected traveling distance of the team and reduce congestions. A Petri net (PN) with outputs models the movement capabilities of the team and the regions of interest. The imposed specification is translated to a set of linear restrictions for some binary variables, the robot movement capabilities are formulated as linear constraints on PN markings, and the evaluations of the binary variables are linked with PN markings via linear inequalities. This allows us to solve an integer linear programming problem whose solution yields robotic trajectories satisfying the task

Collision-free path coordination and cycle time optimization of industrial robot cells

In industry, short ramp-up times, product quality, product customization and high production rates are among the main drivers of technological progress. This is especially true for automotive manufacturers whose market is very competitive, constantly pushing for new solutions. In this industry, many of the processes are carried out by robots: for example, operations such as stud/spot welding, sealing, painting and inspection. Besides higher production rates, the improvement of these processes is important from a sustainability perspective, since an optimized equipment utilization may be achieved, in terms of resources used, including such things as robots, energy, and physical prototyping. The achievements of such goals may, nowadays, be reached also thanks to virtual methods, which make modeling, simulation and optimization of industrial processes possible. The work in this thesis may be positioned in this area and focuses on virtual product and production development for throughput improvement of robotics processes in the automotive industry. Specifically, the thesis presents methods, algorithms and tools to avoid collisions and minimize cycle time in multi-robot stations. It starts with an overview of the problem, providing insights into the relationship between the volumes shared by the robots\u27 workspaces and more abstract modeling spaces. It then describes a computational method for minimizing cycle time when robot paths are geometrically fixed and only velocity tuning is allowed to avoid collisions. Additional requirements are considered for running these solutions in industrial setups, specifically the time delays introduced when stopping robots to exchange information with a programmable logic controller (PLC). A post-processing step is suggested, with algorithms taking into account these practical constraints. When no communication at all with the PLC is highly desirable, a method of providing such programs is described to give completely separated robot workspaces. Finally, when this is not possible (in very cluttered environments and with densely distributed tasks, for example), robot routes are modified by changing the order of operations to avoid collisions between robots.In summary, by requiring fewer iterations between different planning stages, using automatic tools to optimize the process and by reducing physical prototyping, the research presented in this thesis (and the corresponding implementation in software platforms) will improve virtual product and production realization for robotic applications

A graphical formalism for mixed multi-unit combinatorial auctions

Mixed multi-unit combinatorial auctions are auctions that allow participants to bid for bundles of goods to buy, for bundles of goods to sell, and for transformations of goods. The intuitive meaning of a bid for a transformation is that the bidder is offering to produce a set of output goods after having received a set of input goods. To solve such an auction the auctioneer has to choose a set of bids to accept and decide on a sequence in which to implement the associated transformations. Mixed auctions can potentially be employed for the automated assembly of supply chains of agents. However, mixed auctions can be effectively applied only if we can also ensure their computational feasibility without jeopardising optimality. To this end, we propose a graphical formalism, based on Petri nets, that facilitates the compact represention of both the search space and the solutions associated with the winner determination problem for mixed auctions. This approach allows us to dramatically reduce the number of decision variables required for solving a broad class of mixed auction winner determination problems. An additional major benefit of our graphical formalism is that it provides new ways to formally analyse the structural and behavioural properties of mixed auctions. © 2009 Springer Science+Business Media, LLC.This work was funded by the Jose Castillejo programme (JC2008-00337), IEA (TIN2006-15662-C02-01), OK (IST-4-027253-STP), eREP(EC-FP6-CIT5-28575) and Agreement Technologies (CONSOLIDER CSD2007-0022, INGENIO 2010)Peer Reviewe

Multi-Quality Auto-Tuning by Contract Negotiation

A characteristic challenge of software development is the management of omnipresent change. Classically, this constant change is driven by customers changing their requirements. The wish to optimally leverage available resources opens another source of change: the software systems environment. Software is tailored to specific platforms (e.g., hardware architectures) resulting in many variants of the same software optimized for different environments. If the environment changes, a different variant is to be used, i.e., the system has to reconfigure to the variant optimized for the arisen situation. The automation of such adjustments is subject to the research community of self-adaptive systems. The basic principle is a control loop, as known from control theory. The system (and environment) is continuously monitored, the collected data is analyzed and decisions for or against a reconfiguration are computed and realized. Central problems in this field, which are addressed in this thesis, are the management of interdependencies between non-functional properties of the system, the handling of multiple criteria subject to decision making and the scalability. In this thesis, a novel approach to self-adaptive software--Multi-Quality Auto-Tuning (MQuAT)--is presented, which provides design and operation principles for software systems which automatically provide the best possible utility to the user while producing the least possible cost. For this purpose, a component model has been developed, enabling the software developer to design and implement self-optimizing software systems in a model-driven way. This component model allows for the specification of the structure as well as the behavior of the system and is capable of covering the runtime state of the system. The notion of quality contracts is utilized to cover the non-functional behavior and, especially, the dependencies between non-functional properties of the system. At runtime the component model covers the runtime state of the system. This runtime model is used in combination with the contracts to generate optimization problems in different formalisms (Integer Linear Programming (ILP), Pseudo-Boolean Optimization (PBO), Ant Colony Optimization (ACO) and Multi-Objective Integer Linear Programming (MOILP)). Standard solvers are applied to derive solutions to these problems, which represent reconfiguration decisions, if the identified configuration differs from the current. Each approach is empirically evaluated in terms of its scalability showing the feasibility of all approaches, except for ACO, the superiority of ILP over PBO and the limits of all approaches: 100 component types for ILP, 30 for PBO, 10 for ACO and 30 for 2-objective MOILP. In presence of more than two objective functions the MOILP approach is shown to be infeasible

Interpretable task planning and learning for autonomous robotic surgery with logic programming

This thesis addresses the long-term goal of full (supervised) autonomy in surgery, characterized by dynamic environmental (anatomical) conditions, unpredictable workflow of execution and workspace constraints. The scope is to reach autonomy at the level of sub-tasks of a surgical procedure, i.e. repetitive, yet tedious operations (e.g., dexterous manipulation of small objects in a constrained environment, as needle and wire for suturing). This will help reducing time of execution, hospital costs and fatigue of surgeons during the whole procedure, while further improving the recovery time for the patients. A novel framework for autonomous surgical task execution is presented in the first part of this thesis, based on answer set programming (ASP), a logic programming paradigm, for task planning (i.e., coordination of elementary actions and motions). Logic programming allows to directly encode surgical task knowledge, representing emph{plan reasoning methodology} rather than a set of pre-defined plans. This solution introduces several key advantages, as reliable human-like interpretable plan generation, real-time monitoring of the environment and the workflow for ready adaptation and failure recovery. Moreover, an extended review of logic programming for robotics is presented, motivating the choice of ASP for surgery and providing an useful guide for robotic designers. In the second part of the thesis, a novel framework based on inductive logic programming (ILP) is presented for surgical task knowledge learning and refinement. ILP guarantees fast learning from very few examples, a common drawback of surgery. Also, a novel action identification algorithm is proposed based on automatic environmental feature extraction from videos, dealing for the first time with small and noisy datasets collecting different workflows of executions under environmental variations. This allows to define a systematic methodology for unsupervised ILP. All the results in this thesis are validated on a non-standard version of the benchmark training ring transfer task for surgeons, which mimics some of the challenges of real surgery, e.g. constrained bimanual motion in small space