Juan Ares

A methodological framework for viewpoint-oriented conceptual modeling

To solve any nontrivial problem, it first needs to be conceptualized, taking into account the individual who has the problem. However, a problem is generally associated with more than one individual, as is usually the case in software development. Therefore, this process has to take into account different viewpoints about the problem and any discrepancies that could arise as a result. Traditionally, conceptualization in software engineering has omitted the different viewpoints that the individuals may have of the problem and has inherently enforced consistency in the event of any discrepancies, which are considered as something to be systematically rejected. The paper presents a methodological framework that explicitly drives the conceptualization of different viewpoints and manages the different types of discrepancies that arise between them, which become really important in the process. The definition of this framework is generic, and it is therefore independent of any particular software development paradigm. Its application to software engineering means that viewpoints and their possible discrepancies can be considered in the software process conceptual modeling phase. This application is illustrated by means of what is considered to be a standard problem: the IFIP case.

An architectural model for software testing lesson learned systems

Context: Software testing is a key aspect of software reliability and quality assurance in a context where software development constantly has to overcome mammoth challenges in a continuously changing environment. One of the characteristics of software testing is that it has a large intellectual capital component and can thus benefit from the use of the experience gained from past projects. Software testing can, then, potentially benefit from solutions provided by the knowledge management discipline. There are in fact a number of proposals concerning effective knowledge management related to several software engineering processes. Objective: We defend the use of a lesson learned system for software testing. The reason is that such a system is an effective knowledge management resource enabling testers and managers to take advantage of the experience locked away in the brains of the testers. To do this, the experience has to be gathered, disseminated and reused. Method: After analyzing the proposals for managing software testing experience, significant weaknesses have been detected in the current systems of this type. The architectural model proposed here for lesson learned systems is designed to try to avoid these weaknesses. This model (i) defines the structure of the software testing lessons learned; (ii) sets up procedures for lesson learned management; and (iii) supports the design of software tools to manage the lessons learned. Results: A different approach, based on the management of the lessons learned that software testing engineers gather from everyday experience, with two basic goals: usefulness and applicability. Conclusion: The architectural model proposed here lays the groundwork to overcome the obstacles to sharing and reusing experience gained in the software testing and test management. As such, it provides guidance for developing software testing lesson learned systems.

A methodological framework for generic conceptualisation: problem-sensitivity in software engineering

Abstract The first step towards developing quality software is to conceptually model the problem raised in its own context. Software engineering, however, has traditionally focused on implementation concepts, and has paid little or no attention to the problem domain. This paper presents a generic methodological framework to guide conceptual modelling, focusing on the problem within its domain. This framework is defined considering aspects related to a generic conceptualisation, and its application to software engineering—illustrated using the IFIP Case—achieves the called-for problem-sensitivity.

Knowledge Management Systems Development: A Roadmap

This paper approaches the Knowledge Management Systems study, focusing not only in the establishment of essential development activities, but also in techniques, technologies, and tools for their support. Despite of the wide range of existing proposals for the development of this type of systems, none of them has achieved a level detailed enough to allow a direct application. This study is intended to be a palliative for the above-mentioned lack of detail by means of a development guide for Knowledge Management Systems. In this way, the proposed solution offers a clear definition concerning what has to be done and which type of mechanisms should be used for its development.

Formal conceptualisation as a basis for a more procedural knowledge management

Knowledge management at an organisational level can only be brought into practice if a corporate memory is defined. Unfortunately, at this moment there is no complete and procedural specification on how to build it. This paper presents a complete and generic knowledge representation scheme that makes it possible to conceptualise/represent the knowledge of any domain in a systematic way, guiding the definition of a corporate memory and allowing us to reach a more procedural level in knowledge management discipline. The conclusions of our study, which follows the generic and formal definition of any conceptualisation, are illustrated by a real project.

Towards a lessons learned system for critical software

Failure can be a major driver for the advance of any engineering discipline and Software Engineering is no exception. But failures are useful only if lessons are learned from them. In this article we aim to make a strong defence of, and set the requirements for, lessons learned systems for safety-critical software. We also present a prototype lessons learned system that includes many of the features discussed here. We emphasize that, apart from individual organizations, lessons learned systems should target industrial sectors and even the Software Engineering community. We would like to encourage the Software Engineering community to use this kind of systems as another tool in the toolbox, which complements or enhances other approaches like, for example, standards and checklists.

A soft computing framework for classifying time series based on fuzzy sets of events

It is not always possible to decide whether or not a part of a time series is an event.The use of event certainty is necessary and useful in those cases.This framework is an evolution of an earlier one used for classifying time series.Our new proposal takes into account the concept of event certainty.The new framework improves the time series classification results of its predecessor. Time series are sequences of data gathered over a period of time that emerge in different domains and whose analysis requires the application of specialized techniques, like, for example, data mining. Many existing time series data mining techniques, like the discrete Fourier transform (DFT), offer solutions for analysing whole time series. Often, however, it is more important to analyse certain regions of interest, known as events, rather than whole time series. Event identification is a highly complex task, as it is not always possible to determine with absolute certainty whether or not a segment of a time series is an event. In such cases, the best practice is to establish the certainty of this segment being a time series event, thus outputting a fuzzy set of events.In this paper we propose a framework that is capable of identifying events and establishing the degree of certainty that a domain expert would assign to the identified events based on a previous training process assisted by a panel of experts. Having identified the events, the proposed framework can be used to classify time series. This is done by means of a process that combines time series comparison and time series reference model generation by analysing the events contained in the respective time series and the certainties of the identified events. The proposed framework is an evolution of an earlier framework that we developed which did not apply soft computing techniques to identify and manage the time series events.We have used our framework to classify times series generated in the electroencephalography (EEG) area. EEG is a neurological exploration used to diagnose nervous system disorders. The performance of the framework was evaluated in terms of classification accuracy. The results confirmed that, thanks to the use of soft computing techniques, the new framework substantially improves the time series classification results of its predecessor.

Definition of a problem-sensitive conceptual modelling language: foundations and application to software engineering

Abstract A conceptual modelling language should provide constructors that can be used to represent the conceptualisation of a problem considering the problem domain. However, software engineering has traditionally focused on implementation concepts. This paper considers the appropriate generic conceptualisation theoretical aspects to identify the conceptual elements for which constructors have to be provided in a problem-sensitive conceptual modelling language. These elements match the formal definition of any conceptualisation and are derived from natural language. By looking at these elements, we have defined a conceptual modelling language that has been successfully applied in knowledge engineering and software engineering.

A Knowledge-Based System for CMM Evaluation

Even though the possession of a high CMM level undoubtedly implies prestige and competitive advantages for a software development organisation, its attainment may imply a considerable economic burden because of potentially necessary audits. It is therefore very interesting to minimise the costs by paying only for the truly indispensable audits. This article proposes a Knowledge-Based System that makes it possible to evaluate an organisation at a determined CMM level and as such limit the services of an auditor to those cases in which the system’s response complies with the requested CMM level and the necessary associated skills. This clearly implies an important cost reduction for audits with a negative result. The design of this system is based on the CommonKADS methodology, and its implementation was carried out with the Clips tool.

A New Approach for the Validation of Conceptual Holonic Constructions

The concepts of holon and holarchy were first applied in the manufacturing world to develop Holonic Manufacturing Systems. Since then, they have been used in many fields and have proved to be applicable concepts for developing applications in any business area. Resulting applications are based on conceptual holonic constructions. Like any model, a holarchy needs to be validated under real circumstances. Such validation assures the quality of the holarchy before it is implemented. In general, validation research tends to target: 1) the specific types of holons handled in each proposal and/or the selected development paradigms; and 2) algorithm performance rather than architecture quality. This paper proposes and evaluates a methodology that focuses on the quality of the architecture. This methodology is able to validate any holonic architecture built to meet trade requirements. Moreover, this is a general-purpose methodology. Therefore, the methodology would be valid for any domain and would not be invalidated by holon types and/or implementation paradigms emerging, changing or falling into disuse. For this purpose, we consider holonic architectures as conceptual models, using the pure holon and holarchy concepts and passing up not only any specific implementation paradigm but also any set of specific holon types.