Sonia Suárez

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.

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.

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.

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.

Holonic Recursiveness with Multi-Agent System Technologies

Recursive Systems are often needed or recommended for automatically deploy or build a software system composed of multiple entities on a large network, or on distributed locations, without a central control. We use the recursive definitions proposed in holonic systems, as the recursiveness is an elemental structural property of their structure. Holonic systems are used to be implemented usingMAS technologies (platforms) on the basis of the shared functional properties of autonomy and cooperation of agents and holons. This paper discusses the adequacy of the actualMAS technologies for holonic recursiveness implementation. A comparison of MAS platforms is done through a framework that will guide the decisions of designers and developers.

Implementation Challenges for Supporting Coworking Virtual Enterprises

The creation of co working alliances is usually restricted to the coworkers in the same workspace. However, they might not necessarily be best partners to take advantage of a collaboration opportunity. To break the spatial constrains and find the best partners irrespective of their co working spaces location, our proposal is to represent and manage the co working alliances as virtual enterprises (VEs) and to develop a multi-agent system (MAS) that serves as Virtual Breeding Environment (VBE). In this paper we provide the basis for the future development of this system. Thus, we introduce the co working VEs distinguishing features and the initial proposals on how to implement the MAS to address the co working VEs life-cycle.

A fuzzy approach for solving a critical benchmarking problem

Nowadays, benchmarking is a widespread technique for evaluating an aspect—process, product, service, etc.—by comparing it against the best in class with the aim of improving this aspect or identifying the best alternative. There have been numerous attempts at defining a rigorous benchmarking process by specifying steps that should be taken to put benchmarking into practice. All these proposals use a method of calculation that treats the weights and ratings of each criterion as numerical variables, even if they are not. This means that the binary and linguistic variables have to be artificially translated to numerical variables, misleading us into thinking that the concepts we are dealing with are quantitative when they really are not. In this paper, we propose a new method of calculation based on fuzzy logic to rectify this key methodological error. Its definition is based on: (i) a new division operator for fuzzy numbers representing conjugated variables, as in the case outlined here; (ii) a new aggregation operator that can integrate binary, numerical and/or linguistic variables; and, finally, (iii) an operator that can translate the final fuzzy rating into the linguistic variable that best represents it. Therefore, the resulting method is: (i) closer to the user since it manages more human-understandable values and (ii) not dependent on the above artificial translation process, which could lead to sizeable variations in the benchmarking result.