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 Practical Machine Learning Tools and Techniques

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

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

This paper reports an empirical study that uses clustering techniques to derive segmented models from software engineering repositories, focusing on the improvement of the accuracy of estimates. In particular, we used two datasets obtained from the International Software Benchmarking Standards Group (ISBSG) repository and created clusters using the M5 algorithm. Each cluster is associated with a linear model. We then compare the accuracy of the estimates so generated with the classical multivariate linear regression and least median squares. Results show that there is an improvement in the accuracy of the results when using clustering. Furthermore, these techniques can help us to understand the datasets better; such techniques provide some advantages to project managers while keeping the estimation process within reasonable complexity.

/pdf/segmentation-of-software-engineering-datasets-using-the-m5-5alak6gtra.pdf

Segmentation of software engineering datasets using the m5 algorithm

Software processes vary across organizations and over time. Managing this process diversity is a delicate balancing act between creative, healthy diversity and chaos. In this paper, we examine a particular aspect of this issue, namely some relationships between diversity in software processes, software evolution and the quality of software products and processes. Our main contribution is to distinguish between two broad kinds of process diversity, which we call latitudinal and longitudinal process diversity. To illustrate the differences between these two, we examine the case of a medium-sized system (50 000 lines of C++ code) which has undergone major changes during its lifetime of 10 years. The software was originally developed by an individual academic using a research-oriented process to develop a standalone proof-of-concept system. In a current multi-team project, involving three industrial and three academic partners, the software has been adapted for integration as a subsystem of a near-market product. We suggest ways in which the observed process diversity seems to be linked to a change in the software's propensity for evolution, and we discuss the impact of this on both product and process quality.

http://www.cvg.reading.ac.uk/~nts/Articles/SiebelCook2003-smr264.pdf

Latitudinal and longitudinal process diversity

We discuss a generic quality framework, based on a generic model, for evaluating Web processes. The aim is to perform assessment and improvement of web processes by using techniques from empirical software engineering. A web development process can be broadly classified into two almost independent sub-processes: the authoring process (AUTH process) and the process of developing the infrastructure (INF process). The AUTH process concerns the creation and management of the contents of a set of nodes and the way they are linked to produce a web application, whereas the INF development process provides technological support and involves creation of databases, integration of the web application to legacy systems etc. In this paper, we instantiate our generic quality model to the AUTH process and present a measurement framework for this process. We also present a tool support to provide effective guidance to software personnel including developers, managers and quality assurance engineers.

A generic model and tool support for assessing and improving Web processes

The characterization of defective modules in software engineering remains a challenge. In this work, we use data mining techniques to search for rules that indicate modules with a high probability of being defective. Using datasets from the PROMISE repository1, we first applied feature selection to work only with those attributes from the datasets capable of predicting defective modules. Then, a genetic algorithm search for rules characterising subgroups with a high probability of being defective. This algorithm overcomes the problem of unbalanced datasets where the number of non-defective samples in the dataset highly outnumbers the defective ones.

Finding Defective Software Modules by Means of Data Mining Techniques

It is an undeniable fact that software project managers need reliable techniques and robust tool support to be able to exercise a fine control over the development process so that products can be delivered in time and within budget. Therefore, managers need to be trained so that they could learn and use new techniques and be aware of their possible impacts. In this context, effective learning is an issue. A small number of empirical studies have been carried out to study the impact of software engineering education. One such study is by Pfahl et al [11] in which they have performed a controlled experiment to evaluate the learning effectiveness of using a process simulation model for educating computer science students in software project management. The experimental group applied a Systems Dynamics simulation model while the control group used the COCOMO model as a predictive tool for project planning. The results indicated that students using the simulation model gain a better understanding about typical behaviour patterns of software development projects. Experiments need to be externally replicated to both verify and generalise original results. In this paper, we will discuss an externally replicated experiment in which we keep the design and the goal of the above experiment intact. We then analyse our results in relation to the original experiment and another externally replicated experiment, discussed in [12].

/pdf/effective-software-project-management-education-through-2vnpub9q5q.pdf

Daniel Rodriguez

Papers

Segmentation of software engineering datasets using the m5 algorithm

Latitudinal and longitudinal process diversity

A generic model and tool support for assessing and improving Web processes

Finding Defective Software Modules by Means of Data Mining Techniques

Effective Software Project Management Education through Simulation Models: an Externally Replicated Experiment