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

HE 5th edition of the Portuguese Software Engineering Doctoral Symposium (SEDES'2014) is held in September 23, 2014, in Guimarães, Portugal, co-located with the QUATIC’2014 conference. The aim of this symposium is to bring together Software Engineering PhD students supervised or co-supervised by faculty members of Portuguese Universities in a constructive environment, where they can present and discuss their ongoing PhD projects, receive feedback and valuable suggestions from faculty and peers, as well as network with other researchers in the field. The symposium aims at selecting PhD students that have already decided on a specific research topic, but are at least one year from delivering their dissertation, so that they can still benefit from the symposium discussions. This symposium is also instrumental as a gathering point for the Software Engineering research community in Portugal, with a tradition of synergies facilitator since its first edition. As such, an effort has been made to hold this event in different towns. Previous editions were held in Coimbra [1], Caparica [2], Porto [3], and Lisboa [4].

Foreword of the 5th Portuguese Software Engineering Doctoral Symposium (SEDES'2014).

Security is a requirement of utmost importance to produce high-quality software. However, there is still a considerable amount of vulnerabilities being discovered and fixed almost weekly. We hypothesize that developers affect the maintainability of their codebases when patching vulnerabilities. This paper evaluates the impact of patches to improve security on the maintainability of open-source software. Maintainability is measured based on the Better Code Hub's model of 10 guidelines on a dataset, including 1300 security-related commits. Results show evidence of a trade-off between security and maintainability for 41.90% of the cases, i.e., developers may hinder software maintainability. Our analysis shows that 38.29% of patches increased software complexity and 37.87% of patches increased the percentage of LOCs per unit. The implications of our study are that changes to codebases while patching vulnerabilities need to be performed with extra care; tools for patch risk assessment should be integrated into the CI/CD pipeline; computer science curricula needs to be updated; and, more secure programming languages are necessary.

Rui Abreu

Papers

Foreword of the 5th Portuguese Software Engineering Doctoral Symposium (SEDES'2014).

Fixing Vulnerabilities Potentially Hinders Maintainability