Isabel María del Águila

Ant Colony Optimization for the Next Release Problem: A Comparative Study

The selection of the enhancements to be included in the next software release is a complex task in every software development. Customers demand their own software enhancements, but all of them cannot be included in the software product, mainly due to the existence limited resources. In most of the cases, it is not feasible to develop all the new functionalities suggested by customers. Hence each new feature competes against each other to be included in the next release. This problem of minimizing development effort and maximizing customers’ satisfaction is known as the next release problem (NRP). In this work we study the NRP problem as an optimisation problem. We use and describe three different meta-heuristic search techniques for solving NRP: simulated annealing, genetic algorithms and ant colony system (specifically, we show how to adapt the ant colony system to NRP). All of them obtain good but possibly sub optimal solution. Also we make a comparative study of these techniques on a case study. Furthermore, we have observed that the sub optimal solutions found applying these techniques include a high percentage of the requirements considered as most important by each individual customer.

Multi-objective ant colony optimization for requirements selection

The selection of a set of requirements between all the requirements previously defined by customers is an important process, repeated at the beginning of each development step when an incremental or agile software development approach is adopted. The set of selected requirements will be developed during the actual iteration. This selection problem can be reformulated as a search problem, allowing its treatment with metaheuristic optimization techniques. This paper studies how to apply Ant Colony Optimization algorithms to select requirements. First, we describe this problem formally extending an earlier version of the problem, and introduce a method based on Ant Colony System to find a variety of efficient solutions. The performance achieved by the Ant Colony System is compared with that of Greedy Randomized Adaptive Search Procedure and Non-dominated Sorting Genetic Algorithm, by means of computational experiments carried out on two instances of the problem constructed from data provided by the experts.

An Intelligent System for Therapy Control in a Distributed Organization

This paper describes a decision-making system for phytosanitary control advicing. The solution adopted consists of developing a Web-accessible information system based on a multi-agent architecture, integrating knowledge-based techniques and classical information analysis and management techniques. CommonKADS was used for the design of some knowledge-based agents. Internet implementation and integration was done in a knowledge-based system implementation environment, with programs executed on the Web server.

AN EXPERTISE MODEL FOR THERAPY PLANNING USING ABDUCTIVE REASONING

Generic reasoning models facilitate the construction of knowledge-based systems for solving complex problems, such as therapy planning in an agricultural context. The basic features of these models are problem-solving methods, which are proposed to carry out the tasks ordinarily done by experts in solving a specific problem. This article describes an expertise model in the domain of plant health, which was obtained applying the CommonKADS methodology. The most important result is the abductive method proposed for supplying a solution to treatment problems in domains where there is no protocol for therapy planning tasks. The description of the method is based on the algebraic formulation of a domain-independent general treatment. Therefore, the abductive method proposed is sufficiently generalized to be applied to different domains.

Bayesian networks for enhancement of requirements engineering: a literature review

AbstractRequirements analysis is the software engineering stage that is closest to the users’ world. It also involves tasks that are knowledge intensive. Thus, the use of Bayesian networks (BNs) to model this knowledge would be a valuable aid. These probabilistic models could manage the imprecision and ambiguities usually present in requirements engineering (RE). In this work, we conduct a literature review focusing on where and how BNs are applied on subareas of RE in order to identify which gaps remain uncovered and which methods might engineers employ to incorporate this intelligent technique into their own requirements processes. The scarcity of identified studies (there are only 20) suggests that not all RE areas have been properly investigated in the literature. The evidence available for adopting BNs into RE is sufficiently mature yet the methods applied are not easily translatable to other topics. Nonetheless, there are enough studies supporting the applicability of synergistic cooperation between RE and BNs. This work provides a background for understanding the current state of research encompassing RE and BNs. Functional, non-functional and -ilities requirements artifacts are enhanced by the use of BNs. These models were obtained by interacting with experts or by learning from databases. The most common criticism from the point of view of BN experts is that the models lack validation, whereas requirements engineers point to the lack of a clear application method for BNs and the lack of tools for incorporating them as built-in help functions.

Olive Fly Infestation Prediction Using Machine Learning Techniques

This article reports on a study on olive-fly infestation prediction using machine learning techniques. . The purpose of the work was, on the one hand, to make accurate predictions and, on the other, to verify whether the Bayesian network techniques are competitive with respect to classification trees. We have applied the techniques to a dataset and, in addition, performed a previous phase of variables selection to simplify the complexity of the classifiers. The results of the experiments show that Bayesians networks produce valid predictors, although improved definition of dependencies and refinement of the variables selection methods are required.

Milestones in Software Engineering and Knowledge Engineering History: A Comparative Review

We present a review of the historical evolution of software engineering, intertwining it with the history of knowledge engineering because “those who cannot remember the past are condemned to repeat it.” This retrospective represents a further step forward to understanding the current state of both types of engineerings; history has also positive experiences; some of them we would like to remember and to repeat. Two types of engineerings had parallel and divergent evolutions but following a similar pattern. We also define a set of milestones that represent a convergence or divergence of the software development methodologies. These milestones do not appear at the same time in software engineering and knowledge engineering, so lessons learned in one discipline can help in the evolution of the other one.

REQUIREMENT RISK LEVEL FORECAST USING BAYESIAN NETWORKS CLASSIFIERS

Requirement engineering is a key issue in the development of a software project. Like any other development activity it is not without risks. This work is about the empirical study of risks of requirements by applying machine learning techniques, specifically Bayesian networks classifiers. We have defined several models to predict the risk level for a given requirement using three dataset that collect metrics taken from the requirement specifications of different projects. The classification accuracy of the Bayesian models obtained is evaluated and compared using several classification performance measures. The results of the experiments show that the Bayesians networks allow obtaining valid predictors. Specifically, a tree augmented network structure shows a competitive experimental performance in all datasets. Besides, the relations established between the variables collected to determine the level of risk in a requirement, match with those set by requirement engineers. We show that Bayesian networks are valid tools for the automation of risks assessment in requirement engineering.

Architecture for the use of synergies between knowledge engineering and requirements engineering

The application of Artificial Intelligence techniques in the processes of Software Engineering is achieving good results in those activities that require the use of expert knowledge. Within Software Engineering, the activities related to requirements become a suitable target for these techniques, since a good or bad execution of these tasks has a strong impact in the quality of the final software product. Hence, a tool to support the decision makers during these activities is highly desired. This work presents a three-layer architecture, which provides a seamless integration between Knowledge Engineering and Requirement Engineering. The architecture is instantiated into a CARE (Computer-Aided Engineering Requirement) tool that integrates some Artificial Intelligence techniques: Requisites, a Bayesian network used to validate the specification of the requirements of a project, and metaheuristic techniques (simulated annealing, genetic algorithm and an ant colony system) to the selection of the requirements that have to be included into the final software product.

Knowledge Model of a Therapy Administration Task – Applied to an Agricultural Domain

This work presents an application of the CommonKADS methodology to define of a knowledge model for a therapy admini- stration task, reusing CommonKADS task templates. A knowledge- based system for solving the problem of phytosanitary strategy selec- tion in greenhouses has been constructed using this knowledge model. As a result, we have defined a therapy administration task template that can be reused to construct additional knowledge models for other knowledge-based systems.