Search

The work described in this paper is part of the development of a framework to support the joint execution of cooperative missions by a group of robotic vehicles, in a simulated, augmented or real environment. Such framework brings forward the need for formal languages in which to specify the group of vehicles that compose a team; the scenario in which they will operate; and the definition of the mission to be performed (surveillance, pollution detection, search and rescue operations, and so on). This paper presents the Mission Description Language (MDL), a XML dialect that describes the specifics of a mission to be performed by a team of autonomous vehicles. The MDL definition decomposes a mission into a number of possibly dependent phases and uses high-level concepts that facilitate the specification of a mission by both experts and non-experts. The dialect was defined using XML Schema, with all the required information being integrated in the definition. An interface was developed and incorporated into the framework, as to allow for the creation and edition of XML files following the defined schema. Once the information is correctly specified, it can be used in the framework, thus facilitating the process of mission specification and deployment. A survey answered by both practitioners and researchers shows that the degree of satisfaction with MDL is elevated (the overall evaluation of MDL achieved a 3.97 score (out of 5), with 82.05% of the answers being equal to or above 4); also, the usability of the interface was evaluated, having achieved a score of 82.9 in the SUS scale. These results imply that MDL is flexible enough to represent several types of missions, through a user-friendly interface.

Ontology verification refers to the activity where an ontology is tested against its ontology requirements to ensure that it is built correctly in compliance with its ontology requirements specification. Therefore, it is an important activity that should be performed in any ontology development process. Since manual verification can be a time-consuming and repetitive task, testing processes to automatically verify an ontology facilitate this activity. Moreover, the involvement of not only ontology engineers during the ontology verification process , but also domain experts and users, can provide valuable feedback to avoid misunderstandings and lack of information. This paper proposes a method for ontology verification that defines the testing activities to be performed. The method uses a testing language based on lexico-syntactic patterns to facilitate the definition of tests and an ontology to store and publish such tests. Moreover, this verification testing method proposes an online tool to execute tests on one or more ontologies. The method was compared in terms of time and errors by user evaluation with other tools for ontology verification; the evaluation showed that the tools that use testing languages had better results in terms of reducing errors in the verification activity compared to the tools that do not.

In this paper, we propose an integrated multi-objective optimization framework for the development and maintenance of a modular software system. From the perspective of development, other than the cost of a software system, the compatibility of the software components, software reliability, delivery time and execution time for the software, we also consider the evaluation of fitness (using TOPSIS) of software components (COTS and in-house) for non-outsourced modules and vendors for outsourced modules on the basis of many important qualitative attributes. On the other hand, from the perspective of maintenance, we consider the software maintenance cost, the evaluation of fitness (using TOPSIS) of maintenance teams (the outsourced teams and the in-house team) on the basis of many important qualitative attributes, and the interactions between maintenance teams, which we call as the customer to customer (C2C) relationship. Moreover, in order to ensure that the software development company may not prefer to interact with a large number of maintenance teams and that a single team is not always burdened with the maintenance of all the modules, we consider some new cardinality and load balancing constraints. The proposed optimization model is tested using a real-world case study involving e-commerce software development.

While working on a software specification, designers usually need to evaluate different architectural alternatives to be sure that quality criteria are met. Even when these quality aspects could be expressed in terms of multiple software metrics, other qualitative factors cannot be numerically measured, but they are extracted from the engineers know-how and prior experiences. In fact, detecting not only strong but also weak points in the different solutions seems to fit better with the way humans make their decisions. Putting the human in the loop brings new challenges to the search-based software engineering field, especially for those human-centered activities within the early analysis phase. This paper explores how the interactive evolutionary computation can serve as a basis for integrating the humans judgment into the search process. An interactive approach is proposed to discover software architectures, in which both quantitative and qualitative criteria are applied to guide a multi-objective evolutionary algorithm. The obtained feedback is incorporated into the fitness function using architectural preferences allowing the algorithm to discern between promising and poor solutions. Experimentation with real users has revealed that the proposed interaction mechanism can effectively guide the search towards those regions of the search space that are of real interest to the expert.

Regression testing is the process of retesting a system after it or its environment has changed. Many techniques aim to find the cheapest subset of the regression test suite that achieves full coverage. More recently, it has been observed that the tester might want to have a range of solutions providing different trade-offs between cost and one or more forms of coverage, this being a multi-objective optimisation problem. This paper further develops the multi-objective agenda by adapting a decomposition-based multi-objective evolutionary algorithm (MOEA/D). Experiments evaluated four approaches: a classic greedy algorithm; non-dominated sorting genetic algorithm II (NSGA-II); MOEA/D with a fixed value for a parameter c ; and MOEA/D in which tuning was used to choose the value of c . These used six programs from the SIR repository and one larger program, VoidAuth. In all of the experiments MOEA/D with tuning was the most effective technique. The relative performance of the other techniques varied, although MOEA/D with fixed c outperformed NSGA-II on the larger programs (Space and VoidAuth).