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

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

Program slicing is a decomposition technique that slides program components not relevant to a chosen computation, referred to as a slicing criterion. The remaining components form an executable program called a slice that computes a projection of the original programpsilas semantics. Using examples coupled with fundamental principles, a tutorial introduction to program slicing is presented. Then applications of program slicing are surveyed, ranging from its first use as a debugging technique to current applications in property verification using finite state models. Finally, a summary of research challenges for the slicing community is discussed.

Program slicing

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.

Big Data: A Survey

A program slice consists of the parts of a program that (potentially) affect the values computed at some point of interest Such a point of interest is referred to as a slicing criterion, and is typically specified by a location in the program in combination with a subset of the program’s variables The task of computing program slices is called program slicing The original definition of a program slice was presented by Weiser in 1979 Since then, various slightly different notions of program slices have been proposed, as well as a number of methods to compute them An important distinction is that between a static and a dynamic slice Static slices are computed without making assumptions regarding a program’s input, whereas the computation of dynamic slices relies on a specific test case This survey presents an overview of program slicing, including the various general approaches used to compute slices, as well as the specific techniques used to address a variety of language features such as procedures, unstructured control flow, composite data types and pointers, and concurrency Static and dynamic slicing methods for each of these features are compared and classified in terms of their accuracy and efficiency Moreover, the possibilities for combining solutions for different features are investigated Recent work on the use of compiler-optimization and symbolic execution techniques for obtaining more accurate slices is discussed The paper concludes with an overview of the applications of program slicing, which include debugging, program integration, dataflow testing, and software maintenance

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A Survey of Program Slicing Techniques.

Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness at meeting some performance goal. Various goals are possible; one involves rate of fault detection, a measure of how quickly faults are detected within the testing process. An improved rate of fault detection during testing can provide faster feedback on the system under test and let software engineers begin correcting faults earlier than might otherwise be possible. One application of prioritization techniques involves regression testing, the retesting of software following modifications; in this context, prioritization techniques can take advantage of information gathered about the previous execution of test cases to obtain test case orderings. We describe several techniques for using test execution information to prioritize test cases for regression testing, including: 1) techniques that order test cases based on their total coverage of code components; 2) techniques that order test cases based on their coverage of code components not previously covered; and 3) techniques that order test cases based on their estimated ability to reveal faults in the code components that they cover. We report the results of several experiments in which we applied these techniques to various test suites for various programs and measured the rates of fault detection achieved by the prioritized test suites, comparing those rates to the rates achieved by untreated, randomly ordered, and optimally ordered suites.

Prioritizing test cases for regression testing

The high cost of locating faults in programs has motivated the development of techniques that assist in fault localization by automating part of the process of searching for faults. Empirical studies that compare these techniques have reported the relative effectiveness of four existing techniques on a set of subjects. These studies compare the rankings that the techniques compute for statements in the subject programs and the effectiveness of these rankings in locating the faults. However, it is unknown how these four techniques compare with Tarantula, another existing fault-localization technique, although this technique also provides a way to rank statements in terms of their suspiciousness. Thus, we performed a study to compare the Tarantula technique with the four techniques previously compared. This paper presents our study---it overviews the Tarantula technique along with the four other techniques studied, describes our experiment, and reports and discusses the results. Our studies show that, on the same set of subjects, the Tarantula technique consistently outperforms the other four techniques in terms of effectiveness in fault localization, and is comparable in efficiency to the least expensive of the other four techniques.

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Empirical evaluation of the tarantula automatic fault-localization technique

One of the most expensive and time-consuming components of the debugging process is locating the errors or faults. To locate faults, developers must identify statements involved in failures and select suspicious statements that might contain faults. This paper presents a new technique that uses visualization to assist with these tasks. The technique uses color to visually map the participation of each program statement in the outcome of the execution of the program with a test suite, consisting of both passed and failed test cases. Based on this visual mapping, a user can inspect the statements in the program, identify statements involved in failures, and locate potentially faulty statements. The paper also describes a prototype tool that implements our technique along with a set of empirical studies that use the tool for evaluation of the technique. The empirical studies show that, for the subject we studied, the technique can be effective in helping a user locate faults in a program.

http://laser.cs.umass.edu/courses/cs521-621/papers/p467-jones.pdf

Visualization of test information to assist fault localization

Regression testing is an expensive but necessary maintenance activity performed on modified software to provide confidence that changes are correct and do not adversely affect other portions of the softwore. A regression test selection technique choses, from an existing test set, thests that are deemed necessary to validate modified software. We present a new technique for regression test selection. Our algorithms construct control flow graphs for a precedure or program and its modified version and use these graphs to select tests that execute changed code from the original test suite. We prove that, under certain conditions, the set of tests our technique selects includes every test from the original test suite that con expose faults in the modified procedfdure or program. Under these conditions our algorithms are safe. Moreover, although our algorithms may select some tests that cannot expose faults, they are at lease as precise as other safe regression test selection algorithms. Unlike many other regression test selection algorithms, our algorithms handle all language constructs and all types of program modifications. We have implemented our algorithms; initial empirical studies indicate that our technique can significantly reduce the cost of regression testing modified software.

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A safe, efficient regression test selection technique

Regression testing is a necessary but expensive maintenance activity aimed at showing that code has not been adversely affected by changes. Regression test selection techniques reuse tests from an existing test suite to test a modified program. Many regression test selection techniques have been proposed, however, it is difficult to compare and evaluate these techniques because they have different goals. This paper outlines the issues relevant to regression test selection techniques, and uses these issues as the basis for a framework within which to evaluate the techniques. The paper illustrates the application of the framework by using it to evaluate existing regression test selection techniques. The evaluation reveals the strengths and weaknesses of existing techniques, and highlights some problems that future work in this area should address.

/pdf/analyzing-regression-test-selection-techniques-1q4n3ktcvt.pdf

Mary Jean Harrold

Papers

Prioritizing test cases for regression testing

Empirical evaluation of the tarantula automatic fault-localization technique

Visualization of test information to assist fault localization

A safe, efficient regression test selection technique

Analyzing regression test selection techniques