Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments.

/pdf/a-survey-of-transfer-learning-w9lk128p94.pdf

A survey of transfer learning

Software architectures shift the focus of developers from lines-of-code to coarser-grained architectural elements and their overall interconnection structure. Architecture description languages (ADLs) have been proposed as modeling notations to support architecture-based development. There is, however, little consensus in the research community on what is an ADL, what aspects of an architecture should be modeled in an ADL, and which of several possible ADLs is best suited for a particular problem. Furthermore, the distinction is rarely made between ADLs on one hand and formal specification, module interconnection, simulation and programming languages on the other. This paper attempts to provide an answer to these questions. It motivates and presents a definition and a classification framework for ADLs. The utility of the definition is demonstrated by using it to differentiate ADLs from other modeling notations. The framework is used to classify and compare several existing ADLs, enabling us, in the process, to identify key properties of ADLs. The comparison highlights areas where existing ADLs provide extensive support and those in which they are deficient, suggesting a research agenda for the future.

/pdf/a-classification-and-comparison-framework-for-software-3so7xapmcq.pdf

A classification and comparison framework for software architecture description languages

The World Wide Web has succeeded in large part because its software architecture has been designed to meet the needs of an Internet-scale distributed hypermedia application. The modern Web architecture emphasizes scalability of component interactions, generality of interfaces, independent deployment of components, and intermediary components to reduce interaction latency, enforce security, and encapsulate legacy systems. In this article we introduce the Representational State Transfer (REST) architectural style, developed as an abstract model of the Web architecture and used to guide our redesign and definition of the Hypertext Transfer Protocol and Uniform Resource Identifiers. We describe the software engineering principles guiding REST and the interaction constraints chosen to retain those principles, contrasting them to the constraints of other architectural styles. We then compare the abstract model to the currently deployed Web architecture in order to elicit mismatches between the existing protocols and the applications they are intended to support.

Principled design of the modern Web architecture

The World Wide Web has succeeded in large part because its software architecture has been designed to meet the needs of an Internet-scale distributed hypermedia system. The modern Web architecture emphasizes scalability of component interactions, generality of interfaces, independent deployment of components, and intermediary components to reduce interaction latency, enforce security, and encapsulate legacy systems. In this paper, we introduce the Representational State Transfer (REST) architectural style, developed as an abstract model of the Web architecture to guide our redesign and definition of the Hypertext Transfer Protocol and Uniform Resource Identifiers. We describe the software engineering principles guiding REST and the interaction constraints chosen to retain those principles, contrasting them to the constraints of other architectural styles. We then compare the abstract model to the currently deployed Web architecture in order to elicit mismatches between the existing protocols and the applications they are intended to support.

/pdf/principled-design-of-the-modern-web-architecture-1hhuhjnc21.pdf

Abbreviated Contents Figures and Tables Preface Introduction and Examples Concepts Relevant to Information Behavior Models, Paradigms, and Theories in the Study of Information Behavior Methods for Studying Information Behavior Research Results and Reflections Appendix: Glossary Appendix: Questions for Discussion and Application References Index

Looking for Information: A Survey of Research on Information Seeking, Needs and Behavior

While a large fraction of application code is devoted to graphical user interface (GUI) functions, support for reuse in this domain has largely been confined to the creation of GUI toolkits ("widgets"). We present a novel architectural style directed at supporting larger grain reuse and flexible system composition. Moreover, the style supports design of distributed, concurrent applications. Asynchronous notification messages and asynchronous request messages are the sole basis for intercomponent communication. A key aspect of the style is that components are not built with any dependencies on what typically would be considered lower-level components, such as user interface toolkits. Indeed, all components are oblivious to the existence of any components to which notification messages are sent. While our focus has been on applications involving graphical user interfaces, the style has the potential for broader applicability. Several trial applications using the style are described.

/pdf/a-component-and-message-based-architectural-style-for-gui-2ne0uqqgy3.pdf

A component- and message-based architectural style for GUI software

We analyze the version history of 7 software systems to predict the most fault prone entities and files. The basic assumption is that faults do not occur in isolation, but rather in bursts of several related faults. Therefore, we cache locations that are likely to have faults: starting from the location of a known (fixed) fault, we cache the location itself, any locations changed together with the fault, recently added locations, and recently changed locations. By consulting the cache at the moment a fault is fixed, a developer can detect likely fault-prone locations. This is useful for prioritizing verification and validation resources on the most fault prone files or entities. In our evaluation of seven open source projects with more than 200,000 revisions, the cache selects 10% of the source code files; these files account for 73%-95% of faults--a significant advance beyond the state of the art

/pdf/predicting-faults-from-cached-history-2mxycx5vzf.pdf

Predicting faults from cached history

Twenty-seven automatically extractable bug fix patterns are defined using the syntax components and context of the source code involved in bug fix changes. Bug fix patterns are extracted from the configuration management repositories of seven open source projects, all written in Java (Eclipse, Columba, JEdit, Scarab, ArgoUML, Lucene, and MegaMek). Defined bug fix patterns cover 45.7% to 63.3% of the total bug fix hunk pairs in these projects. The frequency of occurrence of each bug fix pattern is computed across all projects. The most common individual patterns are MC-DAP (method call with different actual parameter values) at 14.9---25.5%, IF-CC (change in if conditional) at 5.6---18.6%, and AS-CE (change of assignment expression) at 6.0---14.2%. A correlation analysis on the extracted pattern instances on the seven projects shows that six have very similar bug fix pattern frequencies. Analysis of if conditional bug fix sub-patterns shows a trend towards increasing conditional complexity in if conditional fixes. Analysis of five developers in the Eclipse projects shows overall consistency with project-level bug fix pattern frequencies, as well as distinct variations among developers in their rates of producing various bug patterns. Overall, data in the paper suggest that developers have difficulty with specific code situations at surprisingly consistent rates. There appear to be broad mechanisms causing the injection of bugs that are largely independent of the type of software being produced.

/pdf/toward-an-understanding-of-bug-fix-patterns-3o4butlqx4.pdf

Toward an understanding of bug fix patterns

Machine learning classifiers have recently emerged as a way to predict the introduction of bugs in changes made to source code files. The classifier is first trained on software history, and then used to predict if an impending change causes a bug. Drawbacks of existing classifier-based bug prediction techniques are insufficient performance for practical use and slow prediction times due to a large number of machine learned features. This paper investigates multiple feature selection techniques that are generally applicable to classification-based bug prediction methods. The techniques discard less important features until optimal classification performance is reached. The total number of features used for training is substantially reduced, often to less than 10 percent of the original. The performance of Naive Bayes and Support Vector Machine (SVM) classifiers when using this technique is characterized on 11 software projects. Naive Bayes using feature selection provides significant improvement in buggy F-measure (21 percent improvement) over prior change classification bug prediction results (by the second and fourth authors [28]). The SVM's improvement in buggy F-measure is 9 percent. Interestingly, an analysis of performance for varying numbers of features shows that strong performance is achieved at even 1 percent of the original number of features.

/pdf/reducing-features-to-improve-code-change-based-bug-2rdw1olbgm.pdf

Reducing Features to Improve Code Change-Based Bug Prediction

The number of bugs (or fixes) is a common factor used to measure the quality of software and assist bug related analysis. For example, if software files have many bugs, they may be unstable. In comparison, the bug-fix time--the time to fix a bug after the bug was introduced--is neglected. We believe that the bug-fix time is an important factor for bug related analysis, such as measuring software quality. For example, if bugs in a file take a relatively long time to be fixed, the file may have some structural problems that make it difficult to make changes. In this report, we compute the bug-fix time of files in ArgoUML and PostgreSQL by identifying when bugs are introduced and when the bugs are fixed. This report includes bug-fix time statistics such as average bug-fix time, and distributions of bug-fix time. We also list the top 20 bug-fix time files of two projects.

/pdf/how-long-did-it-take-to-fix-bugs-18ozu8z8it.pdf

E. James Whitehead

Papers

A component- and message-based architectural style for GUI software

Predicting faults from cached history

Toward an understanding of bug fix patterns

Reducing Features to Improve Code Change-Based Bug Prediction

How long did it take to fix bugs