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

Data Mining - Concepts and Techniques.

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

This book opens a fresh chapter in the debate on local enterprise clusters and their strategies for upgrading in the global economy. The authors employ a novel conceptual framework in their research on industrial clusters in Europe, Latin America and Asia and provide new perspectives and insights for researchers and policymakers alike.

Governance in global value chains

Software fault localization, the act of identifying the locations of faults in a program, is widely recognized to be one of the most tedious, time consuming, and expensive – yet equally critical – activities in program debugging. Due to the increasing scale and complexity of software today, manually locating faults when failures occur is rapidly becoming infeasible, and consequently, there is a strong demand for techniques that can guide software developers to the locations of faults in a program with minimal human intervention. This demand in turn has fueled the proposal and development of a broad spectrum of fault localization techniques, each of which aims to streamline the fault localization process and make it more effective by attacking the problem in a unique way. In this article, we catalog and provide a comprehensive overview of such techniques and discuss key issues and concerns that are pertinent to software fault localization as a whole.

A Survey on Software Fault Localization

Software engineers make use of design patterns for reasons that range from performance to code comprehensibility. Several design patterns capturing the body of knowledge of best practices have been proposed in the past, namely creational, structural and behavioral patterns. However, with the advent of mobile devices, it becomes a necessity a catalog of design patterns for energy efficiency. In this work, we inspect commits, issues and pull requests of 1027 Android and 756 iOS apps to identify common practices when improving energy efficiency. This analysis yielded a catalog, available online, with 22 design patterns related to improving the energy efficiency of mobile apps. We argue that this catalog might be of relevance to other domains such as Cyber-Physical Systems and Internet of Things. As a side contribution, an analysis of the differences between Android and iOS devices shows that the Android community is more energy-aware.

Catalog of energy patterns for mobile applications

Current metrics for assessing the adequacy of a test-suite plainly focus on the number of components (be it lines, branches, paths) covered by the suite, but do not explicitly check how the tests actually exercise these components and whether they provide enough information so that spectrum-based fault localization techniques can perform accurate fault isolation. We propose a metric, called DDU, aimed at complementing adequacy measurements by quantifying a test-suite's diagnosability, i.e., the effectiveness of applying spectrum-based fault localization to pinpoint faults in the code in the event of test failures. Our aim is to increase the value generated by creating thorough test-suites, so they are not only regarded as error detection mechanisms but also as effective diagnostic aids that help widely-used fault-localization techniques to accurately pinpoint the location of bugs in the system. Our experiments show that optimizing a test suite with respect to DDU yields a 34% gain in spectrum-based fault localization report accuracy when compared to the standard branch-coverage metric.

/pdf/a-test-suite-diagnosability-metric-for-spectrum-based-fault-26l7kuqh1s.pdf

A test-suite diagnosability metric for spectrum-based fault localization approaches

Spectrum-based Bayesian reasoning can effectively rank candidate fault locations based on passing/failing test cases, but the diagnostic quality highly depends on the size and diversity of the underlying test suite. As test suites in practice often do not exhibit the necessary properties, we present a technique to extend existing test suites with new test cases that optimize the diagnostic quality. We apply probability theory concepts to guide test case generation using entropy, such that the amount of uncertainty in the diagnostic ranking is minimized. Our ENTBUG prototype extends the search-based test generation tool EVOSUITE to use entropy in the fitness function of its underlying genetic algorithm, and we applied it to seven real faults. Empirical results show that our approach reduces the entropy of the diagnostic ranking by 49% on average (compared to using the original test suite), leading to a 91% average reduction of diagnosis candidates needed to inspect to find the true faulty one.

/pdf/entropy-based-test-generation-for-improved-fault-48sj2o2lca.pdf

Entropy-based test generation for improved fault localization

Spreadsheets are by far the most prominent example of end-user programs of ample size and substantial structural complexity. In addition, spreadsheets are usually not tested very rigorously and thus comprise faults. Locating faults is a hard task due to the size and the structure, which is usually not directly visible to the user, i.e., the functions are hidden behind the cells and only the computed values are presented. Hence, there is a strong need for debugging support. In this paper, we adapt three program-debugging approaches that have been designed for more traditional procedural or object-oriented programming languages. These techniques are Spectrum-based Fault Localization, Spectrum-Enhanced Dynamic Slicing, and Constraint-based Debugging. Beside the theoretical foundations, we present a more sophisticated empirical evaluation including a comparison of these approaches. The empirical evaluation shows that Sfl (Spectrum-based Fault Localization) and Sendys (Spectrum ENhanced Dynamic Slicing) are the most promising techniques.

https://link.springer.com/content/pdf/10.1007%2F978-3-642-37057-1_6.pdf

On the empirical evaluation of fault localization techniques for spreadsheets

In practically all development processes, regression tests are used to detect the presence of faults after a modification. If faults are detected, a fault localization algorithm can be used to reduce the manual inspection cost. However, while using test case prioritization to enhance the rate of fault detection of the test suite (e.g., statement coverage), the diagnostic information gain per test is not optimal, which results in needless inspection cost during diagnosis. We present RAPTOR, a test prioritization algorithm for fault localization, based on reducing the similarity between statement execution patterns as the testing progresses. Unlike previous diagnostic prioritization algorithms, RAPTOR does not require false negative information, and is much less complex. Experimental results from the Software Infrastructure Repository's benchmarks show that RAPTOR is the best technique under realistic conditions, with average cost reductions of 40% with respect to the next best technique, with negligible impact on fault detection capability.

Rui Abreu

Papers

Catalog of energy patterns for mobile applications

A test-suite diagnosability metric for spectrum-based fault localization approaches

Entropy-based test generation for improved fault localization

On the empirical evaluation of fault localization techniques for spreadsheets

Prioritizing tests for fault localization through ambiguity group reduction