A citation analysis of the ACSC 2006 - 2008 proceedings, with reference to the CORE conference and journal rankings

This paper compares the CORE rankings of computing conferences and journals to the frequency of citation of those journals and conferences in the Australasian Computer Science Conference (ACSC) 2006, 2007 and 2008 proceedings. The assumption underlying this study is that there should be a positive relationship between citation rates and the CORE rankings. Our analysis shows that the CORE rankings broadly reflect the ACSC citations, but with some anomalies. While these anomalies might be minor in the larger scheme of things, anomalies need to be addressed, as the careers of individual academics may depend upon it. Rankings are probably here to stay, and this paper ends with some suggestions on how the rankings process should now evolve, so that it becomes more transparent. Copyright © 2009, Australian Computer Society, Inc

Discovering itemset interactions

Itemsets, which are treated as intermediate results in association mining, have attracted significant research due to the inherent complexity of their generation. However, there is currently little literature focusing upon the interactions between itemsets, the nature of which may potentially contain valuable information. This paper presents a novel tree-based approach to discovering item-set interactions, a task which cannot be undertaken by current association mining techniques

A citation analysis of the ICER 2005-07 proceedings

This paper identifies the most commonly cited conferences, journals and books of the 43 papers within the first three ICER proceedings. A large array of conferences, journals, and books were cited. However, only a small set of journals and conferences were cited frequently, and the majority were only cited within a single paper, which is consistent with a power law distribution, as predicted by Zipf's Law. The most commonly cited books are concerned with education in general (29%) or psychology (20%), while 17% of books are concerned with computer science education and 12% with computing content. The citation results for ICER are contrasted with earlier published citation analyses of SIGCSE 2007 and ACE2005-07. © 2009, Australian Computer Society, Inc

Inference of gene expression networks using memetic gene expression programming

In this paper we aim to infer a model of genetic networks from time series data of gene expression profiles by using a new gene expression programming algorithm. Gene expression networks are modelled by differential equations which represent temporal gene expression relations. Gene Expression Programming is a new extension of genetic programming. Here we combine a local search method with gene expression programming to form a memetic algorithm in order to find not only the system of differential equations but also fine tune its constant parameters. The effectiveness of the proposed method is justified by comparing its performance with that of conventional genetic programming applied to this problem in previous studies

Metascheduling and Heuristic Co-Allocation Strategies in Distributed Computing

In this paper, we address problems of efficient computing in distributed systems with non-dedicated resources including utility grid. There are global job flows from external users along with resource owner's local tasks upon the resource non-dedication condition. Competition for resource reservation between independent users, local and global job flows substantially complicates scheduling and the requirement to provide the necessary quality of service. A metascheduling concept, justified in this work, assumes a complex combination of job flow dispatching and application-level scheduling methods for parallel jobs, as well as resource sharing and consumption policies established in virtual organizations and based on economic principles. We introduce heuristic slot selection and co-allocation strategies for parallel jobs. They are formalized by given criteria and implemented by algorithms of linear complexity on an available slots number

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Exploiting Wikipedia Semantics for Computing Word Associations

Semantic association computation is the process of automatically quantifying the strength of a semantic connection between two textual units based on various lexical and semantic relations such as hyponymy (car and vehicle) and functional associations (bank and manager). Humans have can infer implicit relationships between two textual units based on their knowledge about the world and their ability to reason about that knowledge. Automatically imitating this behavior is limited by restricted knowledge and poor ability to infer hidden relations. Various factors affect the performance of automated approaches to computing semantic association strength. One critical factor is the selection of a suitable knowledge source for extracting knowledge about the implicit semantic relations. In the past few years, semantic association computation approaches have started to exploit web-originated resources as substitutes for conventional lexical semantic resources such as thesauri, machine readable dictionaries and lexical databases. These conventional knowledge sources suffer from limitations such as coverage issues, high construction and maintenance costs and limited availability. To overcome these issues one solution is to use the wisdom of crowds in the form of collaboratively constructed knowledge sources. An excellent example of such knowledge sources is Wikipedia which stores detailed information not only about the concepts themselves but also about various aspects of the relations among concepts. The overall goal of this thesis is to demonstrate that using Wikipedia for computing word association strength yields better estimates of humans' associations than the approaches based on other structured and unstructured knowledge sources. There are two key challenges to achieve this goal: first, to exploit various semantic association models based on different aspects of Wikipedia in developing new measures of semantic associations; and second, to evaluate these measures compared to human performance in a range of tasks. The focus of the thesis is on exploring two aspects of Wikipedia: as a formal knowledge source, and as an informal text corpus. The first contribution of the work included in the thesis is that it effectively exploited the knowledge source aspect of Wikipedia by developing new measures of semantic associations based on Wikipedia hyperlink structure, informative-content of articles and combinations of both elements. It was found that Wikipedia can be effectively used for computing noun-noun similarity. It was also found that a model based on hybrid combinations of Wikipedia structure and informative-content based features performs better than those based on individual features. It was also found that the structure based measures outperformed the informative content based measures on both semantic similarity and semantic relatedness computation tasks. The second contribution of the research work in the thesis is that it effectively exploited the corpus aspect of Wikipedia by developing a new measure of semantic association based on asymmetric word associations. The thesis introduced the concept of asymmetric associations based measure using the idea of directional context inspired by the free word association task. The underlying assumption was that the association strength can change with the changing context. It was found that the asymmetric association based measure performed better than the symmetric measures on semantic association computation, relatedness based word choice and causality detection tasks. However, asymmetric-associations based measures have no advantage for synonymy-based word choice tasks. It was also found that Wikipedia is not a good knowledge source for capturing verb-relations due to its focus on encyclopedic concepts specially nouns. It is hoped that future research will build on the experiments and discussions presented in this thesis to explore new avenues using Wikipedia for finding deeper and semantically more meaningful associations in a wide range of application areas based on humans' estimates of word associations

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Generic Quality-Aware Refactoring and Co-Refactoring in Heterogeneous Model Environments

Software has been subject to change, at all times, in order to make parts of it, for instance, more reusable, better to understand by humans, or to increase efficiency under a certain point of view. Restructurings of existing software can be complex. To prevent developers from doing this manually, they got tools at hand being able to apply such restructurings automatically. These automatic changes of existing software to improve quality while preserving its behaviour is called refactoring. Refactoring is well investigated for programming languages and mature tools exist for executing refactorings in integrated development environments (IDEs). In recent years, the development paradigm of Model-Driven Software Development (MDSD) became more and more popular and we experience a shift in the sense that development artefacts are considered as models which conform metamodels. This can be understood as abstraction, which resulted in the trend that a plethora of new so-called model-based Domain-Specific Languages (DSLs) arose. DSLs have become an integral part in the MDSD and it is obvious that models are subject to change, as well. Thus, refactoring support is required for DSLs in order to prevent users from doing it manually. The problem is that the amount of DSLs is huge and refactorings should not be implemented for new for each of them, since they are quite similar from an abstract viewing. Existing approaches abstract from the target language, which is not flexible enough because some assumptions about the languages have to be made and arbitrary DSLs are not supported. Furthermore, the relation between a strategy which finds model deficiencies that should be improved, a resolving refactoring, and the improved quality is only implicit. Focussing on a particular quality and only detecting those deficiencies deteriorating this quality is difficult, and elements of detected deficient structures cannot be referred to in the resolving refactoring. In addition, heterogeneous models in an IDE might be connected physically or logically, thus, they are dependent. Finding such connections is difficult and can hardly be achieved manually. Applying a restructuring in a model implied by a refactoring in a dependent model must also be a refactoring, in order to preserve the meaning. Thus, this kind of dependent refactorings require an appropriate abstraction mechanism, since they must be specified for dependent models of different DSLs. The first contribution, Role-Based Generic Model Refactoring, uses role models to abstract from refactorings instead of the target languages. Thus, participating structures in a refactoring can be specified generically by means of role models. As a consequence, arbitrary model-based DSLs are supported, since this approach does not make any assumptions regarding the target languages. Our second contribution, Role-Based Quality Smells, is a conceptual framework and correlates deficiencies, their deteriorated qualities, and resolving refactorings. Roles are used to abstract from the causing structures of a deficiency, which then are subject to resolving refactorings. The third contribution, Role-Based Co-Refactoring, employs the graph-logic isomorphism to detect dependencies between models. Dependent refactorings, which we call co-refactorings, are specified on the basis of roles for being independent from particular target DSLs. All introduced concepts are implemented in our tool Refactory. An evaluation in different scenarios complements the thesis. It shows that role models emerged as very powerful regarding the reuse of generic refactorings in arbitrary languages. Role models are suited as an interface for certain structures which are to be refactored, scanned for deficiencies, or co-refactored. All of the presented approaches benefit from it.:List of Figures xv List of Tables xvii List of Listings xix 1. Introduction 1 1.1. Language-Tool Generation Without Consideration Of Time And Space . . . . . 4 1.2. Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3. Generic Quality-Aware Refactoring and Co-Refactoring in Heterogeneous Model Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2. Foundations 15 2.1. Refactoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2. Model-Driven Software Development . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.1. Levels of Abstraction and Metamodelling . . . . . . . . . . . . . . . . . 17 2.2.2. Model Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3. Role-Based Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3. Related Work 23 3.1. Model Refactoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.1.1. Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.1.2. Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.1.3. Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.2. Determination of Quality-Related De ciencies . . . . . . . . . . . . . . . . . . . 32 3.2.1. Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.2.2. Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.2.3. Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.3. Co-Refactoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.3.1. Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.3.2. Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.3.3. Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4. Role-Based Generic Model Refactoring 51 4.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.2. Specifying Generic Refactorings with Role Models . . . . . . . . . . . . . . . . . 53 4.2.1. Specifying Structural Constraints using Role Models . . . . . . . . . . . 55 4.2.2. Mapping Roles to Language Concepts Using Role Mappings . . . . . . . 57 4.2.3. Specifying Language-Independent Transformations using Refactoring Speci cations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.2.4. Composition of Refactorings . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.3. Preserving Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5. Suggesting Role Mappings as Concrete Refactorings 73 5.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.2. Automatic Derivation of Suggestions for Role Mappings with Graph Querying . 74 5.3. Reduction of the Number of Valid Matches . . . . . . . . . . . . . . . . . . . . . 76 5.4. Comparison to Model Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6. Role-Based Quality Smells as Refactoring Indicator 79 6.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.2. Correlating Model De ciencies, Qualities and Refactorings . . . . . . . . . . . . 80 6.2.1. Quality Smell Repository . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 6.2.2. Quality Smell Calculation Repository . . . . . . . . . . . . . . . . . . . . 85 6.3. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 6.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 7. A Quality Smell Catalogue for Android Applications 89 7.1. Quality Smell Catalogue Schema . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 7.2. Acquiring Quality Smells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 7.3. Structure-Based Quality Smells—A Detailed Example . . . . . . . . . . . . . . . 92 7.3.1. The Pattern Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 7.3.2. Quality Smell: Interruption from Background . . . . . . . . . . . . . . . 93 7.4. Quality Smells for Android Applications . . . . . . . . . . . . . . . . . . . . . . 96 7.4.1. Quality Smell: Data Transmission Without Compression . . . . . . . . . 96 7.4.2. Quality Smell: Dropped Data . . . . . . . . . . . . . . . . . . . . . . . . 98 7.4.3. Quality Smell: Durable WakeLock . . . . . . . . . . . . . . . . . . . . . 98 7.4.4. Quality Smell: Internal Use of Getters/Setters . . . . . . . . . . . . . . . 99 7.4.5. Quality Smell: No Low Memory Resolver . . . . . . . . . . . . . . . . . 101 7.4.6. Quality Smell: Rigid AlarmManager . . . . . . . . . . . . . . . . . . . . 101 7.4.7. Quality Smell: Unclosed Closeable . . . . . . . . . . . . . . . . . . . . . 102 7.4.8. Quality Smell: Untouchable . . . . . . . . . . . . . . . . . . . . . . . . . 103 7.5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 8. Role-Based Co-Refactoring in Multi-Language Development Environments 105 8.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 8.2. Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 8.3. Dependency Knowledge Base . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 8.3.1. Categories of Model Dependencies . . . . . . . . . . . . . . . . . . . . . 108 8.3.2. When to Determine Model Dependencies . . . . . . . . . . . . . . . . . 110 8.3.3. How to Determine Model Dependencies . . . . . . . . . . . . . . . . . . 111 8.4. Co-Refactoring Knowledge Base . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 8.4.1. Specifying Coupled Refactorings with Co-Refactoring Speci cations . . 114 8.4.2. Specifying Bindings for Co-Refactorings . . . . . . . . . . . . . . . . . . 116 8.4.3. Determination of Co-Refactoring Speci cations . . . . . . . . . . . . . . 118 8.5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 8.6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 9. Refactory: An Eclipse Tool For Quality-Aware Refactoring and Co-Refactoring 121 9.1. Refactoring Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 9.1.1. Role Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 9.1.2. Refactoring Speci cation . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 9.1.3. Role Model Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 9.1.4. Refactoring Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 9.1.5. Custom Refactoring Extensions . . . . . . . . . . . . . . . . . . . . . . . 129 9.1.6. Pre- and Post-conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 9.1.7. Integration Into the Eclipse Refactoring Framework . . . . . . . . . . . . 130 9.2. Quality Smell Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 9.3. Co-Refactoring Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 9.3.1. Concrete Syntax of a CoRefSpec . . . . . . . . . . . . . . . . . . . . . . . 138 9.3.2. Expression Evaluation by Using an Expression Language . . . . . . . . . 138 9.3.3. UI and Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 9.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 10. Evaluation 143 10.1. Case Study: Reuse of Generic Refactorings in many DSLs . . . . . . . . . . . . . 143 10.1.1. Threats to validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 10.1.2. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 10.1.3. Experience Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 10.2. Case Study: Suggestion of Valid Role Mappings . . . . . . . . . . . . . . . . . . 147 10.2.1. Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 10.2.2. Evaluation and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 151 10.3. Proof of Concept: Co-Refactoring OWL and Ecore Models . . . . . . . . . . . . 155 10.3.1. Coupled OWL-Ecore Refactorings . . . . . . . . . . . . . . . . . . . . . 156 10.3.2. Realisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 10.3.3. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 11. Summary, Conclusion and Outlook 161 11.1. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 11.2. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 11.3. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Appendix 169 A. List of Role Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 B. Comparison to Role Feature Model . . . . . . . . . . . . . . . . . . . . . . . . . 171 C. Complete List of Role Mappings . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 D. List of all IncPL Patterns for Detecting Quality Smells . . . . . . . . . . . . . . . 176 E. Post-Processor of the Extract CompositeState refactoring for UML State Machines 183 F. Speci cation of the Conference Language . . . . . . . . . . . . . . . . . . . . . . 185 List of Abbreviations 187 Bibliography 19

Transparent and adaptive application partitioning using mobile objects

The dynamic nature and heterogeneity of modern execution environments such as mobile, ubiquitous, and grid computing, present major challenges for the development and efficient execution of the applications targeted for these environments. In particular, applications tailored to run in a specific environment will show different and most likely sub-optimal behaviour when executed on a different and/or dynamic environment. Consequently, there has been growing interests in the area of application adaptation which aims to enable applications to cope with the varying execution environments. Adaptive application partitioning, a specific form of non-functional adaptation involving distribution of mobile objects across multiple host machines, is of particular interest to this thesis due to the diversity of its uses. In this approach, certain runtime information (known as context) is used to allow an object-oriented application to adaptively (re)adjust the placement of its objects during its execution, for purposes such as improving application performance and reliability as well as balancing resource utilisation across machines. Promoting the adoption of such adaptation requires a process that requires minimal human involvement in both the execution and the development of the relevant application. These challenges establish the main goals and contributions of this work, which include: 1) Proposing an effective application partitioning solution via the adoption of a decentralised adaptation strategy known as local adaptation. 2) Enabling adaptive application partitioning which does not require human intervention, through automatic collection of required information/context. 3) Proposing a solution for transparently injecting the required adaptation functionality into regular object-oriented applications allowing significant reduction of the associated development cost/effort. The proposed solutions have been implemented in a Java-based adaptation framework called MobJeX. This implementation, which was used as a test bed for the empirical experiments undertaken in this study, can be used to facilitate future research relevant to this particular study