Knowledge Discovery Metamodel Research Articles

Dynamic analysis of quantum annealing programs

Quantum software engineering is emerging as a relevant field, as it deals with the challenges of producing the new quantum software, whose adoption is steadily increasing. Quantum annealing software has gained a certain market penetration, demonstrating a good performance for optimization problems. However, there are no reverse engineering techniques with which to discover the underlying optimization problem definitions (the Hamiltonian functions to be minimized). Problem definitions are, in turn, dynamically defined using classical software, and can evolve over time, which make their accurate comprehension and abstract representation difficult. This paper, therefore, presents a dynamic analysis technique for D-Wave (Python) programs with which to reverse Hamiltonian expressions, and which are additionally represented according to the Knowledge Discovery Metamodel. The usage of this standard makes it possible to represent the reversed Hamiltonians in combination with other parts of classical–quantum software systems. In order to facilitate its adoption, the proposed technique has been empirically validated through a case study with 27 D-Wave programs that demonstrates its effectiveness and efficiency. The main implication of this research is that the proposed technique helps modernize quantum annealing software alongside hybrid software systems.

QRev: migrating quantum code towards hybrid information systems

Quantum computing is now a reality, and its incomparable computational power has led companies to show a great interest in being able to work with quantum software in order to support part of their current and future business operations. However, the quantum computing paradigm differs significantly from its classical counterparts, which has brought about the need to revolutionise how the future software is designed, built, and operated in order to work with quantum computers. Since companies cannot discard all their current (and probably mission-critical) information systems, they must adapt their classical information systems to new specific quantum applications, thus evolving towards hybrid information systems. Unfortunately, there are no specific methods with which to deal with this challenge. We believe that reengineering, and more specifically, software modernisation using model-driven engineering principles, could be useful as regard migrating classical systems and existing quantum programs towards hybrid information systems. This paper, therefore, presents QRev, a reverse engineering tool that analyses quantum programs developed in Q# in order to identify its components and interrelationships, and then generates abstract models that can be used in software modernisation processes. The platform-independent models are generated according to the Knowledge Discovery Metamodel (KDM) standard. QRev is validated in a case study involving five quantum programs in order to demonstrate its effectiveness and scalability. The main implication of the study is that QRev can be used in order to attain KDM models, which can subsequently be employed to restructure or add new quantum functionality at a higher abstraction level, i.e. independently of the specific quantum technology.

Architectural conformance checking for KDM-represented systems

Architecture-Driven Modernization (ADM) is a model-driven reengineering where systems are represented as instances of Knowledge Discovery Metamodel (KDM). KDM is the standard for representing systems in ADM context due to its power for capturing an extensive set of information about software systems. Besides, it is language and platform-independent, so every technique that is able of processing it also present this advantage. A recurrent activity in modernization projects is checking the conformance between the Current Architecture (CA) against the Planned Architecture (PA) in order to identify architectural drifts. The canonical phases of this activity are: (i) specification of the PA with its communication constraints; (ii) ex-traction of the CA, including the relationships among the architectural abstractions; and (iii) comparison between both architectures to identify the drifts. To the best of our knowledge, there is no ACC approach that addresses ACC in ADM context, considering KDM-represented systems. Therefore, we presents an ACC approach to be used in ADM context. We show how KDM can be used in ACC processes for representing the system to be modernized, the PA and the CA. We evaluated Arch-KDM using a small (LabSys-7KLoc) and a medium-size system (FreeMind-84KLoc) and the accuracy of the identification was acceptable.

A method for transforming knowledge discovery metamodel to ArchiMate models

Enterprise architecture has become an important driver to facilitate digital transformation in companies, since it allows to manage IT and business in a holistic and integrated manner by establishing connections among technology concerns and strategical/motivational ones. Enterprise architecture modelling is critical to accurately represent business and their IT assets in combination. This modelling is important when companies start to manage their enterprise architecture, but also when it is remodelled so that the enterprise architecture is realigned in a changing world. Enterprise architecture is commonly modelled by few experts in a manual way, which is error-prone and time-consuming and makes continuous realignment difficult. In contrast, other enterprise architecture modelling proposal automatically analyses some artefacts like source code, databases, services, etc. Previous automated modelling proposals focus on the analysis of individual artefacts with isolated transformations toward ArchiMate or other enterprise architecture notations and/or frameworks. We propose the usage of Knowledge Discovery Metamodel (KDM) to represent all the intermediate information retrieved from information systems’ artefacts, which is then transformed into ArchiMate models. Thus, the core contribution of this paper is the model transformation between KDM and ArchiMate metamodels. The main implication of this proposal is that ArchiMate models are automatically generated from a common knowledge repository. Thereby, the relationships between different-nature artefacts can be exploited to get more complete and accurate enterprise architecture representations.

Software modernization to embrace quantum technology

Quantum Computing is becoming an increasingly mature area, with a simultaneous escalation of investment in many sectors. Quantum technology will revolutionize all the engineering fields. For example, companies will need to add quantum computing progressively to some or all of their daily operations. It is clear that all existing classical information systems cannot be done away with. Rather than that occurring, it is expected that some quantum algorithms will be added, so that they can work alongside classical information systems. There has been no systematic solution offered to deal with this challenge so far. This research proposes a software modernization approach (model-driven reengineering) designed to restructure classical systems to work in conjunction with quantum systems, thereby providing target environments that combine both of these computational paradigms. The approach proposed is systematic, and based on existing software engineering standards like the Knowledge Discovery Metamodel and the Unified Modelling Language. It could therefore be applied in industry in a way that complies with the existing software evolution processes. The independence of this proposal with respect to quantum programming environments is also guaranteed, making its application feasible in the changing environment in today's quantum industry. The main implication of this approach is technical, but also economic, since it enables the reuse of the knowledge embedded in legacy systems, while at the same time the new quantum-based projects are speeded up.

Specification and use of concern metrics for supporting modularity-oriented modernizations

Bad modularized concerns are a known deficiency of legacy systems, making their maintenance increasingly harder and expensive. An alternative is to conduct a modularity-oriented modernization (MOM), whose goal is to restructure concerns for making them more modular. Architecture-driven modernization (ADM) is a model-based alternative for that—it is a kind of reengineering that employs standard metamodels along the process. Knowledge Discovery Metamodel (KDM) is the main one, and it is used for representing all aspects of an existing system to be modernized. ADM also delivers a metamodel called Structured Metrics Metamodel (SMM) whose goal is to standardize the specification of metrics, measurement processes, and results of the measurements. A central task in any modernization process is to measure, characterizing quantitatively, the legacy and the modernized version to check if problems were solved/assuaged. In a MOM, it is important to measure to what extent the concerns are spread across the system using concern metrics. However, there are no studies that investigate how SMM can be used to specify concern metrics in MOMs. Therefore, we present our approach of how to use SMM for specifying concern metrics in a modularity-oriented modernization in the ADM context, i.e., using KDM and SMM. We detail the specification of two concern metrics: Concern Diffusion over Operations (CDO) and Concern Diffusion over Components (CDC). Besides, we also present a tool that can be used in a MOM to support the measurement process

Developing a model-driven reengineering approach for migrating PL/SQL triggers to Java: A practical experience

Model-driven software engineering (MDE) techniques are not only useful in forward engineering scenarios, but can also be successfully applied to evolve existing systems. RAD (Rapid Application Development) platforms emerged in the nineties, but the success of modern software technologies motivated that a large number of enterprises tackled the migration of their RAD applications, such as Oracle Forms. Our research group has collaborated with a software company in developing a solution to migrate PL/SQL monolithic code on Forms triggers and program units to Java code separated in several tiers.Our research focused on the model-driven reengineering process applied to develop the migration tool for the conversion of PL/SQL code to Java. Legacy code is represented in form of KDM (Knowledge-Discovery Metamodel) models. In this paper, we propose a software process to implement a model-driven re-engineering. This process integrates a TDD-like approach to incrementally develop model transformations with three kinds of validations for the generated code. The implementation and validation of the re-engineering approach are explained in detail, as well as the evaluation of some issues related with the application of MDE.

Evaluating the extension mechanisms of the knowledge discovery metamodel for aspect-oriented modernizations

Crosscutting concerns are an intrinsic problem of legacy systems, hindering their maintenance and evolution. A possible solution is to modernize these systems employing aspect-orientation, which provides suitable abstractions for modularizing these kind of concerns. Architecture-Driven Modernization is a more specific kind of software reengineering focused on employing standard metamodels along the whole process, promoting interoperability and reusability across different tools/vendors. Its main metamodel is the Knowledge Discovery Metamodel (KDM), which is able to represent a significant amount of system details. However, up to this moment, there is no extension of this metamodel for aspect-orientation, preventing software engineers from conducting Aspect-Oriented Modernizations. Therefore, in this paper we present our experience on creating a heavyweight and a lightweight extension of KDM for aspect-orientation. We conducted two evaluations. The first one showed all aspect-oriented concepts were represented in both extensions. The second one was a experiment, in which we have analyzed the productivity of software engineers using both extensions. The results showed that the heavyweight extension propitiate a more productive environment in terms of time and number of errors when compared to the lightweight one.

Controlling Meta-Model Extensibility in Model-Driven Engineering

Model-driven engineering (MDE) considers the systematic use of models in software development. A model must be specified through a well-defined modeling language with precise syntax and semantics. In MDE, this syntax is defined by a meta-model. While meta-models tend to be fixed, there are several scenarios that require the customization of existing meta-models. For example, standards of the object management group (OMG) like the knowledge discovery meta-model (KDM) or the diagram definition (DD) are based on the extension of base meta-models according to certain rules. However, these rules are not “ operational ” but are described in natural language and therefore not supported by tools. Although modeling is an activity regulated by meta-models, currently there are no commonly accepted mechanisms to regulate how meta-models can be extended. Hence, in order to solve this problem, we propose a mechanism that allows specifying customization and extension rules for meta-models, as well as a tool that makes it possible to customize the meta-models according to such rules. The tool is based on the Eclipse modeling framework, has been implemented as an Eclipse plugin, and has been validated to guide the extension of OMG standard meta-models, such as KDM and DD.

Alternatives to the Knowledge Discovery Metamodel: An Investigation

To better understand and exploit the knowledge necessary to comprehend and evolve an existing system, different models can be extracted from it. Models represent the extracted information at various abstraction levels, and are useful to document, maintain, and reengineer the system. The Knowledge Discovery Metamodel (KDM) has been defined by the object management group as a meta-model supporting a large share of reverse engineering activities. Its specification has also been adopted by the ISO in 2012. This paper explores and describes alternative meta-models proposed in the literature to support reverse engineering, program comprehension, and software evolution activities. We focus on the similarity and differences of the alternative meta-models with KDM, trying to understand the potentials of reciprocal information interchange. We describe KDM and other five meta-models, plus their extensions available in the literature and their diffusion in the reverse engineering community. We also investigate the approaches using KDM and the five meta-models. In the paper, we underline the limited reuse of models for reverse engineering, and identify potential directions for future related research, to enhance the existing models and ease the exchange of information among them.

Extending the Knowledge Discovery Metamodel for architecture-driven simulation modernization

With the rapid improvement of hardware, the constant evaluation of simulation development methodologies and environments has brought us a new challenge: simulations modernization. This paper presents a simulation knowledge discovery metamodel that extends the Object Management Group’s knowledge discovery metamodel by adding a simulation model package for enabling architecture-driven simulation modernization. While there are some endeavors that propose integration methods, modernization of legacy simulations has not been investigated. Architecture-driven modernization has been introduced as a process of comprehending and transforming existing software assets. It advocates a model-based approach to software modernization in which the knowledge extracted from software assets is captured in models that conform to a metamodel, namely the knowledge discovery metamodel. It specifies an ontology of software assets. Model transformations are then recommended as means of modernization of legacy assets. But diversity in methodologies and approaches to specify simulation modeling assets prohibits the Knowledge Discovery Metamodel from providing adequate meta definitions to capture knowledge in simulations. System Entity Structure has long been used for knowledge representation by the simulation community. It provides formalism for composition, taxonomy and coupling relations. Hence, System Entity Structure is proposed as an intermediate metamodel to capture the meta-constructs and their relations. It is then made available to define particular metamodels for particular simulation modeling methodologies or approaches. The promoted methodology is exercised with samples in order to collect evidence of its applicability. Metamodel segments are presented for knowledge discovery for discrete event systems specification and Modelica simulation programming language. A complete metamodel is then introduced for the real-time distributed simulation domain model of the German Aerospace Center (DLR) Institute of Flight Systems. Finally, this metamodel is used to introduce discovery artifacts. These samples provide valuable indications that the methodology works.

A combined approach for concern identification in KDM models

Abstract Background Systems are considered legacy when their maintenance costs raise to unmanageable levels, but they still deliver valuable benefits for companies. One intrinsic problem of this kind of system is the presence of crosscutting concerns in their architecture, hindering its comprehension and evolution. Architecture-driven modernization (ADM) is the new generation of reengineering in which models are used as main artifacts during the whole process. Using ADM, it is possible to modernize legacy systems by remodularizing their concerns in a more modular shape. In this sense, the first step is the identification of source code elements that contribute to the implementation of those concerns, a process known as concern mining. Although there exist a number of concern mining approaches in the literature, none of them are devoted to ADM, leading individual groups to create their own ad hoc proprietary solutions. In this paper, we propose an approach called crosscutting-concern knowledge discovery meta-model (CCKDM) whose goal is to mine crosscutting concerns in ADM context. Our approach employs a combination of a concern library and a K-means clustering algorithm. Methods We have conducted an experimental study composed of two analyses. The first one aimed to identify the most suitable levenshtein values to apply the clustering algorithm. The second one aimed to check the recall and precision of our approach when compared to oracles and also to two other existing mining techniques (XScan and Timna) found in literature. Results The main result of this work is a combined mining approach for KDM that enables a concern-oriented modernization to be performed. As a secondary and more general result, this work shows that it is possible to adapt existing concern mining code-level approaches for being used in ADM processes and maintain the same level of precision and recall. Conclusions By using the approach herein presented, it was possible to conclude the following: (i) it is possible to automate the identification of crosscutting concerns in KDM models and (ii) the results are similar or equal to other approaches.

Extracting term units and fact units from existing databases using the Knowledge Discovery Metamodel

The extraction of business vocabulary is one of the main tasks in discovering business knowledge implemented in a software system. In this paper we present a model-driven approach to the extraction of business vocabularies from databases of existing software systems. We describe a transformation framework for obtaining the Knowledge Discovery Metamodel based representation of data structure and define an algorithm for the extraction of candidates for business vocabulary entries (i.e. Term and Fact Units) from the representation. The extracted candidates may be further refined by business analysts and used for the identification of business scenarios and rules in software systems.

Business process archeology using MARBLE

Context Legacy information systems age over time. These systems cannot be thrown away because they store a significant amount of valuable business knowledge over time, and they cannot be entirely replaced at an acceptable cost. This circumstance is similar to that of the monuments of ancient civilizations, which have aged but still hold meaningful information about their civilizations. Evolutionary maintenance is the most suitable mechanism to deal with the software ageing problem since it preserves business knowledge. But first, recovering the underlying business knowledge in legacy systems is necessary in order to preserve this vital heritage. Objective This paper proposes and validates a method for recovering and rebuilding business processes from legacy information systems. This method, which can be considered a business process archeology, makes it possible to preserve the business knowledge in legacy information systems. Method The business process archeology method is framed in MARBLE, a generic framework based on Architecture-Driven Modernization (ADM), which uses the Knowledge Discovery Metamodel (KDM) standard. The proposed method is validated using a case study that involves a real-life legacy system. The case study is conducted following the case study protocol proposed by Brereton et al. Results The study reports that the proposed method makes it possible to obtain business process models from legacy systems with adequate levels of accuracy. In addition, the effectiveness of the proposed method is also validated positively. Conclusion The proposed method semi-automatically rebuilds the hidden business processes embedded in a legacy system. Therefore, the business process archeology method quickly allows business experts to have a meaningful understanding of the organization’s business processes. This proposal is less time-consuming and more exhaustive (since it considers the embedded business knowledge) than a manual process redesign by experts from scratch. In addition, it helps maintainers to extract the business knowledge needed for the system to evolve.

Knowledge Discovery Metamodel-ISO/IEC 19506: A standard to modernize legacy systems

Legacy systems age over time as a consequence of uncontrolled maintenance, thus they must be evolved while its valuable embedded knowledge is preserved. Software modernization, and particularly Architecture-Driven Modernization, has become the best solution in the legacy systems’ evolution. ADM defines the Knowledge Discovery Metamodel specification, now being adopted as ISO/IEC 19506 by the International Standards Organization. The KDM metamodel allows to represent all the software artifacts recovered during reverse engineering techniques at different abstraction levels. This paper presents how to use KDM to modernize legacy systems, making them more agile, preserving the embedded business knowledge and reducing maintenance costs.

Integration of Semantic Web and Knowledge Discovery for Enhanced Information Retrivel

management is a process which comprises knowledge discovery, knowledge collection, knowledge organization and knowledge process. Among these four process knowledge discovery is integrated with semantic web for enhanced information retrivel. Knowledge discovery is process of automatically searching large volume of data for patterns that can be considered knowledge about This is described as deriving knowledge from input Knowledge discovery is defined as the non-trivial extraction of implicit, unknown, and potentially useful information from data. Knowledge discovery is one of key component of knowledge management system. Today's World Wide Web has large volume of data - billions of document. So it is a time consuming process to discover effective knowledge from input Here define knowledge discovery meta model (KDM) which defines an ontology for software and their relationships for purpose of