23,556 research outputs found

    Aspect-oriented refactoring of Java programs

    Get PDF

    A heuristic-based approach to code-smell detection

    Get PDF
    Encapsulation and data hiding are central tenets of the object oriented paradigm. Deciding what data and behaviour to form into a class and where to draw the line between its public and private details can make the difference between a class that is an understandable, flexible and reusable abstraction and one which is not. This decision is a difficult one and may easily result in poor encapsulation which can then have serious implications for a number of system qualities. It is often hard to identify such encapsulation problems within large software systems until they cause a maintenance problem (which is usually too late) and attempting to perform such analysis manually can also be tedious and error prone. Two of the common encapsulation problems that can arise as a consequence of this decomposition process are data classes and god classes. Typically, these two problems occur together – data classes are lacking in functionality that has typically been sucked into an over-complicated and domineering god class. This paper describes the architecture of a tool which automatically detects data and god classes that has been developed as a plug-in for the Eclipse IDE. The technique has been evaluated in a controlled study on two large open source systems which compare the tool results to similar work by Marinescu, who employs a metrics-based approach to detecting such features. The study provides some valuable insights into the strengths and weaknesses of the two approache

    SystemC Through the Looking Glass : Non-Intrusive Analysis of Electronic System Level Designs in SystemC

    Get PDF
    Due to the ever increasing complexity of hardware and hardware/software co-designs, developers strive for higher levels of abstractions in the early stages of the design flow. To address these demands, design at the Electronic System Level (ESL) has been introduced. SystemC currently is the de-facto standard for ESL design. The extraction of data from system designs written in SystemC is thereby crucial e.g. for the proper understanding of a given system. However, no satisfactory support of reflection/introspection of SystemC has been provided yet. Previously proposed methods for this purpose %introduced to achieve the goal nonetheless either focus on static aspects only, restrict the language means of SystemC, or rely on modifications of the compiler and/or parser. In this thesis, approaches that overcome these limitations are introduced, allowing the extraction of information from a given SystemC design without changing the SystemC library or the compiler. The proposed approaches retrieve both, static and dynamic (i.e. run-time) information

    Participation in the Environmental Information Exchange Network Using the National Emission Inventory Dataflow

    Get PDF
    The US Environmental Protection Agency (EPA) and state environmental agencies have implemented a large nationwide system termed the Environmental Information Exchange Network, intended to consolidate and standardize the mechanism in which environmental data is exchanged between states, EPA, and other environmental organizations. The Exchange Network infrastructure is based on XML, Web services, and the Internet. The State of Alaska Department of Environmental Conservation (DEC) has an interest in participating in the Exchange Network. This project involves creation of software to extract, transform, validate, and submit DEC\u27s air emissions data to EPA through the Exchange Network. Development of this software also represents a case study of the viability, benefits, and problems when transitioning an existing data exchange to a Service Oriented Architecture (SOA)

    Machine learning and its applications in reliability analysis systems

    Get PDF
    In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

    Automatically generating complex test cases from simple ones

    Get PDF
    While source code expresses and implements design considerations for software system, test cases capture and represent the domain knowledge of software developer, her assumptions on the implicit and explicit interaction protocols in the system, and the expected behavior of different modules of the system in normal and exceptional conditions. Moreover, test cases capture information about the environment and the data the system operates on. As such, together with the system source code, test cases integrate important system and domain knowledge. Besides being an important project artifact, test cases embody up to the half the overall software development cost and effort. Software projects produce many test cases of different kind and granularity to thoroughly check the system functionality, aiming to prevent, detect, and remove different types of faults. Simple test cases exercise small parts of the system aiming to detect faults in single modules. More complex integration and system test cases exercise larger parts of the system aiming to detect problems in module interactions and verify the functionality of the system as a whole. Not surprisingly, the test case complexity comes at a cost -- developing complex test cases is a laborious and expensive task that is hard to automate. Our intuition is that important information that is naturally present in test cases can be reused to reduce the effort in generation of new test cases. This thesis develops this intuition and investigates the phenomenon of information reuse among test cases. We first empirically investigated many test cases from real software projects and demonstrated that test cases of different granularity indeed share code fragments and build upon each other. Then we proposed an approach for automatically generating complex test cases by extracting and exploiting information in existing simple ones. In particular, our approach automatically generates integration test cases from unit ones. We implemented our approach in a prototype to evaluate its ability to generate new and useful test cases for real software systems. Our studies show that test cases generated with our approach reveal new interaction faults even in well tested applications. We evaluated the effectiveness of our approach by comparing it with the state of the art test generation techniques. The evaluation results show that our approach is effective, it finds relevant faults differently from other approaches that tend to find different and usually less relevant faults
    corecore