物件導向軟體之架構(Object-Oriented Software Construction)探討

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Open Innovation: The New Imperative for Creating and Profiting from Technology

Knowledge Management

The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

Today’s business processes are often controlled and supported by information systems. These systems record real-time information about business processes during their executions. This enables the analysis at runtime of the process behavior. However, many modern systems produce “big data”, i.e., collections of data sets so large and complex that it becomes impossible to store and process all of them. Moreover, few processes are in steady-state but, due to changing circumstances, they evolve and systems need to adapt continuously. In this paper, we present a novel framework for the discovery of LTL-based declarative process models from streaming event data in settings where it is impossible to store all events over an extended period of time or where processes evolve while being analyzed. The framework continuously updates a set of valid business constraints based on the events occurred in the event stream. In addition, our approach is able to provide meaningful information about the most significant concept drifts, i.e., changes occurring in a process during its execution. We report about experimental results obtained using synthetic logs and a real-life event log pertaining to the treatment of patients diagnosed with cancer in a large Dutch academic hospital.

Online Discovery of Declarative Process Models from Event Streams

Context Software systems continuously change for various reasons, such as adding new features, fixing bugs, or refactoring. Changes may either increase the source code complexity and disorganization, or help to reducing it. Aim This paper empirically investigates the relationship of source code complexity and disorganization--measured using source code change entropy--with four factors, namely the presence of refactoring activities, the number of developers working on a source code file, the participation of classes in design patterns, and the different kinds of changes occurring on the system, classified in terms of their topics extracted from commit notes. Method We carried out an exploratory study on an interval of the life-time span of four open source systems, namely ArgoUML, Eclipse-JDT, Mozilla, and Samba, with the aim of analyzing the relationship between the source code change entropy and four factors: refactoring activities, number of contributors for a file, participation of classes in design patterns, and change topics. Results The study shows that (i) the change entropy decreases after refactoring, (ii) files changed by a higher number of developers tend to exhibit a higher change entropy than others, (iii) classes participating in certain design patterns exhibit a higher change entropy than others, and (iv) changes related to different topics exhibit different change entropy, for example bug fixings exhibit a limited change entropy while changes introducing new features exhibit a high change entropy. Conclusions Results provided in this paper indicate that the nature of changes (in particular changes related to refactorings), the software design, and the number of active developers are factors related to change entropy. Our findings contribute to understand the software aging phenomenon and are preliminary to identifying better ways to contrast it.

How changes affect software entropy: an empirical study

Today’s business processes are often controlled and supported by information systems. These systems record real-time information about business processes during their executions. This enables the analysis at runtime of the process behavior. However, many modern systems produce “big data”, i.e., collections of data sets so large and complex that it becomes impossible to store and process all of them. Moreover, few processes are in steady-state and due to changing circumstances processes evolve and systems need to adapt continuously. In this paper, we present a novel framework for the discovery of LTL-based declarative process models from streaming event data in settings where it is impossible to store all events over an extended period or where processes evolve while being analyzed. The framework continuously updates a set of valid business constraints based on the events occurred in the event stream. In addition, our approach is able to provide meaningful information about the most significant concept drifts, i.e., changes occurring in a process during its execution. We report about experimental results obtained using logs pertaining the health insurance claims handling in a travel agency.

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Online Process Discovery to Detect Concept Drifts in LTL-Based Declarative Process Models

Cross-system bug fixing propagation is frequent among systems having similar characteristics, using a common framework, or, in general, systems with cloned source code fragments. While previous studies showed that clones tend to be properly maintained within a single system, very little is known about cross-system bug management.This paper describes an approach to mine explicitly documented cross-system bug fixings, and to relate their occurrences to social characteristics of contributors discussing through the project mailing lists--e.g., degree, betweenness, and brokerage--as well as to the contributors' activity on source code.The paper reports results of an empirical study carried out on FreeBSD and OpenBSD kernels. The study shows that the phenomenon of cross-system bug fixing between these two projects occurs often, despite the limited overlap of contributors. The study also shows that cross-system bug fixings mainly involve contributors with the highest degree, betweenness and brokerage level, as well as contributors that change the source code more than others.

Marta Cimitile

Papers

Online Discovery of Declarative Process Models from Event Streams

How changes affect software entropy: an empirical study

Online Process Discovery to Detect Concept Drifts in LTL-Based Declarative Process Models

Social interactions around cross-system bug fixings: the case of FreeBSD and OpenBSD

A systematic review on Deep Learning approaches for IoT security