Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

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

Well adapted to the loosely coupled nature of distributed interaction in large-scale applications, the publish/subscribe communication paradigm has recently received increasing attention. With systems based on the publish/subscribe interaction scheme, subscribers register their interest in an event, or a pattern of events, and are subsequently asynchronously notified of events generated by publishers. Many variants of the paradigm have recently been proposed, each variant being specifically adapted to some given application or network model. This paper factors out the common denominator underlying these variants: full decoupling of the communicating entities in time, space, and synchronization. We use these three decoupling dimensions to better identify commonalities and divergences with traditional interaction paradigms. The many variations on the theme of publish/subscribe are classified and synthesized. In particular, their respective benefits and shortcomings are discussed both in terms of interfaces and implementations.

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The many faces of publish/subscribe

Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

In May 1999 the ACM Council adopted a resolution both reaffirming ACM’s commitment to solving the software quality problem and also stating its opposition to licensing software engineers, on the grounds that licensing is premature and would be ineffective in addressing the software quality problem. ACM Council also affirmed its interest in developing a core body of knowledge for software engineering, which is often viewed as an appealing step toward improving software quality. Our committee was formed to study the existing software engineering body of knowledge efforts — including SWEBOK, with which ACM is involved through the joint IEEE CS/ACM Software Engineering Coordinating Committee (SWECC). Our charge was to determine the status, progress, and likely outcome of these efforts. This report documents our findings, our reasoning, and our conclusions.

An Assessment of Software Engineering Body of Knowledge Efforts

During software development, the sooner a developer learns how code changes affect program analysis results, the more helpful that analysis is. Manually invoking an analysis may interrupt the developer’s workflow or cause a delay before the developer learns the implications of the change. A better approach is continuous analysis tools that always provide up-to-date results. We present Codebase Replication, a technique that eases the implementation of continuous analysis tools by converting an existing offline analysis into an IDE-integrated, continuous tool with two desirable properties: isolation and currency. Codebase Replication creates and keeps in sync a copy of the developer’s codebase. The analysis runs on the copy codebase without disturbing the developer and without being disturbed by the developer’s changes. We developed Solstice, an open-source, publicly-available Eclipse plug-in that implements Codebase Replication. Solstice has less than 2.5 milliseconds overhead for most common developer actions. We used Solstice to implement four Eclipse-integrated continuous analysis tools based on the offline versions of FindBugs, PMD, data race detection, and unit testing. Each conversion required on average 710 LoC and 20 hours of implementation effort. Case studies indicate that Solstice-based continuous analysis tools are intuitive and easy-to-use.

/pdf/reducing-feedback-delay-of-software-development-tools-via-be4wvzpwux.pdf

Reducing Feedback Delay of Software Development Tools via Continuous Analysis

The field of program analysis has made significant improvements recently, but still faces some major obstacles. In this talk I argue that considering analysis as applying longitudinally across the multitude of versions created during a program's lifetime -rather than to a given instance of a program - shows significant promise in overcoming some of these obstacles. I focus on identifying a set of opportunities that arise when this shift in outlook is taken.Most program analysis techniques have focused on questions of the form "Does program P satisfy a given property A?" or "What program points in P satisfy a given property A?" Type-checking is the classic example of the first form, while lexical, syntactic, and semantic analyses are examples of the second form. The key point (with respect to this talk) is that a single program P is being analyzed.Some analyses expand this view and explicitly consider a pair of programs, P and P', where P' represents a modified version of P. Test selection and prioritization techniques are among the best examples of this approach: the idea is to analyze the delta between P and P', and to use that information to determine which test cases must be re-run (for test selection) or should be re-run (for test prioritization). (There are dozens of results in these areas; Harrold et al.'s empirical study is one recent example of test selection [1], and the recent work at Microsoft Research is an example of test prioritization [2].There are at least three ways in which a longitudinal approach could improve analysis.Second, we can use previously computed information to better inform analysis on a newer version. One recent example of this is the work by Kim and Porter that uses historical information about the application of tests of a set of versions as a basis for test prioritization algorithms [3].Third, we can imagine applying otherwise "intractable" analyses over the lifetime of (multiple versions of) a program, as opposed to the (much more limited) time available to analyze a specific version. In essence, there is an opportunity to compute the analysis in stages, with the goal of completing the analysis by specific important points in the program lifetime (e.g., external releases). Work on vertical staging of analyses for runtime compilation is one place to look for ideas and techniques for this kind of "horizontal" staging [4].The traditional view of software evolution says that (to accommodate needed change) program structure degrades and program size increases [5][6]; this in turn tends to increase the difficult of analysis. I propose here some opportunities for viewing time and change as potential benefits with respect to analysis, rather than as roadblocks. This provides potential for significantly improving software dependability over time.

Longitudinal program analysis

This thesis addresses the problem of changing requirements in software evolution. It presents a method of development and change based on roles, where a role, in object oriented development, is a part of an object that addresses a particular concern or requirement. The concept of a role was originally used in design analysis by Trygve Reenskaug. In this research, we extend it's use into implementation.
The contributions of the research include a development process that takes a set of use case requirements and produces an implementation composed of role components, a set of implementation idioms that separate functional concerns from structural and compositional concerns, and several diagrams to bridge the gap between abstract design and concrete implementation. The feasability of the approach is demonstrated with an efficient method of implementation using C++ templates.

Role-oriented programming for software evolution

Object-oriented tests consist of sequences of method invocations. Behavior of an invocation depends on the state of the receiver object and method arguments at the beginning of the invocation. Existing tools for automatic generation of object-oriented test suites, such as Jtest and JCrasher for Java, typically ignore object states. These tools generate redundant tests that exercise the same method behavior, which increases the testing time without increasing the ability to detect faults. We propose a formal framework for detecting redundant tests and present five fully automatic techniques within this framework. Based on these techniques, we have developed a test-minimization tool that removes redundant tests from test suites and a test-generation tool that iteratively augments test suites with non-redundant tests. We evaluate our tools on eight subjects taken from a variety of sources. The experimental results show that our test minimization can remove over 90% of the tests generated by Jtest for most subjects and 30% of the tests generated by JCrasher for half of the subjects, without decreasing the quality of test suites. The results also show that our test generation can effectively generate new tests that increase the quality of test suites generated by Jtest and JCrasher.

David Notkin

Papers

An Assessment of Software Engineering Body of Knowledge Efforts

Reducing Feedback Delay of Software Development Tools via Continuous Analysis

Longitudinal program analysis

Role-oriented programming for software evolution

Improving Generation of Object-Oriented Test Suites by Avoiding Redundant Tests