The use of non-formal information in reverse engineering and software reuse

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Within the field of software maintenance, both reverse engineering and software reuse have been suggested as ways of salvaging some of the investment made in software that is now out of date. One goal that is shared by both reverse engineering and reuse is a desire to be able to redescribe source code, that is to produce higher level descriptions of existing code. The fundamental theme of this thesis is that from a maintenance perspective, source code should be considered primarily as a text. This emphasizes its role as a medium for communication between humans rather than as a medium for human-computer communication. Characteristic of this view is the need to incorporate the analysis of non-formal information, such as comments and identifier names, when developing tools to redescribe code. Many existing tools fail to do this. To justify this text-based view of source code, an investigation into the possible use of non-formal information to index pieces of source code was undertaken. This involved attempting to assign descriptors that represent the code's function to pieces of source code from IBM's CICS project. The results of this investigation support the view that the use of nonformal information can be of practical value in redescribing source code. However, the results fail to suggest that using non-formal information will overcome any of the major difficulties associated with developing tools to redescribe code. This is used to suggest future directions for research

A Network Model for Adaptive Information Retrieval

This thesis presents a network model which can be used to represent Associative Information Retrieval applications at a conceptual level. The model presents interesting characteristics of adaptability and it has been used to model both traditional and knowledge based Information Retrieval applications. Moreover, three different processing frameworks which can be used to implement the conceptual model are presented. They provide three different ways of using domain knowledge to adapt the user formulated query to the characteristics of a specific application domain using the domain knowledge stored in a sub-network. The advantages and drawbacks of these three adaptive retrieval strategies are pointed out and discussed. The thesis also reports the results of an experimental investigation into the effectiveness of the adaptive retrieval given by a processing framework based on Neural Networks. This processing framework makes use of the learning and generalisation capabilities of the Backpropagation learning procedure for Neural Networks to build up and use application domain knowledge in the form of a sub-symbolic knowledge representation. The knowledge is acquired from examples of queries and relevant documents of the collection in use. In the tests reported in this thesis the Cranfield document collection has been used. Three different learning strategies are introduced and analysed. Their results in terms of learning and generalisation of the application domain knowledge are studied from an Information Retrieval point of view. Their retrieval results are studied and compared with those obtained by a traditional retrieval approach. The thesis concludes with a critical analysis of the results obtained in the experimental investigation and with a critical view of the operational effectiveness of such an approach

Emergent behavior of soil fungal dynamics:influence of soil architecture and water distribution

Macroscopic measurements and observations in two-dimensional soil-thin sections indicate that fungal hyphae invade preferentially the larger, air-filled pores in soils. This suggests that the architecture of soils and the microscale distribution of water are likely to influence significantly the dynamics of fungal growth. Unfortunately, techniques are lacking at present to verify this hypothesis experimentally, and as a result, factors that control fungal growth in soils remain poorly understood. Nevertheless, to design appropriate experiments later on, it is useful to indirectly obtain estimates of the effects involved. Such estimates can be obtained via simulation, based on detailed micron-scale X-ray computed tomography information about the soil pore geometry. In this context, this article reports on a series of simulations resulting from the combination of an individual-based fungal growth model, describing in detail the physiological processes involved in fungal growth, and of a Lattice Boltzmann model used to predict the distribution of air-liquid interfaces in soils. Three soil samples with contrasting properties were used as test cases. Several quantitative parameters, including Minkowski functionals, were used to characterize the geometry of pores, air-water interfaces, and fungal hyphae. Simulation results show that the water distribution in the soils is affected more by the pore size distribution than by the porosity of the soils. The presence of water decreased the colonization efficiency of the fungi, as evinced by a decline in the magnitude of all fungal biomass functional measures, in all three samples. The architecture of the soils and water distribution had an effect on the general morphology of the hyphal network, with a "looped" configuration in one soil, due to growing around water droplets. These morphologic differences are satisfactorily discriminated by the Minkowski functionals, applied to the fungal biomass

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Estimation of quantitative descriptors of northeastern Mediterranean karst behavior: multiparametric study and local validation of the Siou-Blanc massif (Toulon, France)

International audienceKey parameters controlling the recharge and behavior of Mediterranean karsts were selected in order to make a quantitative description of northeastern Mediterranean karsts on a regional scale. The methodology was applied to an actual karstic aquifer on the Siou-Blanc Plateau (France). For the recharge study, it was observed that the average yearly rainfall value and δ18O measurements in springs can be considered as good descriptors of climatic variations observed in the Mediterranean area. They can be used to estimate the intake area and the infiltration coefficient. A comparison with a numerical (double permeability) flow model (MODFLOW) on the Siou-Blanc karst improves these exponential relations between effective rainfall and δ18O measurements. Infiltrated water, which flows though different rock types, induces contrasts in the water chemistry. An instantaneous physical and chemical analysis of all the springs of the Siou-Blanc aquifer displays the same expected functioning and variations as had been forecast using the conceptual scheme. Thus, it can be applied to wide areas associated with a northeastern Mediterranean climate for a first approach of a karst study; such a model enables a useful estimation of recharge and behavior with few simple data

Three-dimensional quantitative characterization of grapes morphology and possible relation with grey mould susceptibility

Grey mould is one of the most important diseases of grapevine in the Mediterranean regions caused by the fungi Botrytis cinerea. Many factors are responsible for this disease among them, the morphology of grapes plays a crucial role in grey mould infection. The grapes with highly compact berries are the most susceptible to infection. The common methods applied to evaluate the compactness of grapes cannot apply to grapevine bunches from the same variety. Therefore, novel methods are used to detect compactness by image processing analyses such as photogrammetry for 3D model reconstruction. This study proposes an alternative analysis of bunch morphology and compaction assessment based on virtual 3D models. Seventeen Pinot Gris clones and six Pinot Noir clones were manually collected at harvest time, and the grey mould severity evaluation was carried out in the field. All the grapes were photographed at different angulations, and the 3D model reconstruction was performed by the photogrammetry technique. Several measures and indexes were extracted from each bunch. Principal component analysis (PCA) and two multiple linear regression models (MLR) were applied to identify the descriptors of the clones most related to grey mould infection. The first model assessed the correlation between the grey mould severity and the descriptors from the 2D analysis, while the second model analyzed both descriptors from the 2D and 3D analysis. The 3D MLR presented higher performances than the 2D MLR. The R-square value (R2) and the root mean square error (RMSE) were compared between models. For Pinot Gris, the R2 rose from 0.656 to 0.838, moving from the 2D to the 3D MLR, while the RMSE decreased from 1.713 to 1.175. In Pinot Noir, the 2D model did not provide sufficient robustness, while the proposed MLR estimated R2 with 0.936 value and RMSE with 0.29 value. Additional studies were performed by analyzing the data with graphs and statistics. Consequently, the most significant traits include the estimated empty volume, the width of the grape, weight, volume, shape, and the ratio between surface and height.Grey mould is one of the most important diseases of grapevine in the Mediterranean regions caused by the fungi Botrytis cinerea. Many factors are responsible for this disease among them, the morphology of grapes plays a crucial role in grey mould infection. The grapes with highly compact berries are the most susceptible to infection. The common methods applied to evaluate the compactness of grapes cannot apply to grapevine bunches from the same variety. Therefore, novel methods are used to detect compactness by image processing analyses such as photogrammetry for 3D model reconstruction. This study proposes an alternative analysis of bunch morphology and compaction assessment based on virtual 3D models. Seventeen Pinot Gris clones and six Pinot Noir clones were manually collected at harvest time, and the grey mould severity evaluation was carried out in the field. All the grapes were photographed at different angulations, and the 3D model reconstruction was performed by the photogrammetry technique. Several measures and indexes were extracted from each bunch. Principal component analysis (PCA) and two multiple linear regression models (MLR) were applied to identify the descriptors of the clones most related to grey mould infection. The first model assessed the correlation between the grey mould severity and the descriptors from the 2D analysis, while the second model analyzed both descriptors from the 2D and 3D analysis. The 3D MLR presented higher performances than the 2D MLR. The R-square value (R2) and the root mean square error (RMSE) were compared between models. For Pinot Gris, the R2 rose from 0.656 to 0.838, moving from the 2D to the 3D MLR, while the RMSE decreased from 1.713 to 1.175. In Pinot Noir, the 2D model did not provide sufficient robustness, while the proposed MLR estimated R2 with 0.936 value and RMSE with 0.29 value. Additional studies were performed by analyzing the data with graphs and statistics. Consequently, the most significant traits include the estimated empty volume, the width of the grape, weight, volume, shape, and the ratio between surface and height

A Framework for the Semantics-aware Modelling of Objects

The evolution of 3D visual content calls for innovative methods for modelling shapes based on their intended usage, function and role in a complex scenario. Even if different attempts have been done in this direction, shape modelling still mainly focuses on geometry. However, 3D models have a structure, given by the arrangement of salient parts, and shape and structure are deeply related to semantics and functionality. Changing geometry without semantic clues may invalidate such functionalities or the meaning of objects or their parts. We approach the problem by considering semantics as the formalised knowledge related to a category of objects; the geometry can vary provided that the semantics is preserved. We represent the semantics and the variable geometry of a class of shapes through the parametric template: an annotated 3D model whose geometry can be deformed provided that some semantic constraints remain satisfied. In this work, we design and develop a framework for the semantics-aware modelling of shapes, offering the user a single application environment where the whole workflow of defining the parametric template and applying semantics-aware deformations can take place. In particular, the system provides tools for the selection and annotation of geometry based on a formalised contextual knowledge; shape analysis methods to derive new knowledge implicitly encoded in the geometry, and possibly enrich the given semantics; a set of constraints that the user can apply to salient parts and a deformation operation that takes into account the semantic constraints and provides an optimal solution. The framework is modular so that new tools can be continuously added. While producing some innovative results in specific areas, the goal of this work is the development of a comprehensive framework combining state of the art techniques and new algorithms, thus enabling the user to conceptualise her/his knowledge and model geometric shapes. The original contributions regard the formalisation of the concept of annotation, with attached properties, and of the relations between significant parts of objects; a new technique for guaranteeing the persistence of annotations after significant changes in shape's resolution; the exploitation of shape descriptors for the extraction of quantitative information and the assessment of shape variability within a class; and the extension of the popular cage-based deformation techniques to include constraints on the allowed displacement of vertices. In this thesis, we report the design and development of the framework as well as results in two application scenarios, namely product design and archaeological reconstruction