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

การวจยเชงคณภาพ เปนเครองมอสำคญอยางหนงสำหรบทำความเขาใจสงคมและพฤตกรรมมนษย การวจยแบบการสรางทฤษฎจากขอมล กเปนหนงในหลายระเบยบวธการวจยเชงคณภาพทกำลงไดรบความสนใจ และเปนทนยมเพมสงขนเรอยๆ จากนกวชาการ และนกวจยในสาขาสงคมศาสตร และศาสตรอนๆ เชน พฤตกรรมศาสตร สงคมวทยา สาธารณสขศาสตร พยาบาลศาสตร จตวทยาสงคม ศกษาศาสตร รฐศาสตร และสารสนเทศศกษา ดงนน หนงสอเรอง “ConstructingGrounded Theory: A Practical Guide through Qualitative Analysis” หรอ “การสรางทฤษฎจากขอมล:แนวทางการปฏบตผานการวเคราะหเชงคณภาพ” จะชวยใหผอานมความรความเขาใจถงพฒนาการของปฏบตการวจยแบบสรางทฤษฎจากขอมล ตลอดจนแนวทาง และกระบวนการปฏบตการวจยอยางเปนระบบ จงเปนหนงสอทควรคาแกการอานโดยเฉพาะนกวจยรนใหม เพอเปนแนวทางในการนำความรความเขาใจไประยกตในงานวจยของตน อกทงนกวจยผเชยวชาญสามารถอานเพอขยายมโนทศนดานวจยใหกวางขวางขน

Constructing Grounded Theory: A Practical Guide through Qualitative Analysis

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

Will Tracz, our esteemed editor and Used-Program salesman, has written an entertaining, non-technical book dealing with the practice (and lack of) of software reuse. Its a collection of essays, mostly rehashed (reused?) and updated from various columns and papers published over the years.. Its a short (a bit over 200 pages) easy reading and enjoyable book (I read most of it in one sitting). Some of the essays discuss what was printed in the past and a discussion of the current status of the points.

Safeware: System Safety and Computers

https://onlinelibrary.wiley.com/doi/pdf/10.1002/stvr.247

Problem frames: analyzing and structuring software development problems. By Michael Jackson. Published by Addison‐Wesley, Boston, Massachusetts, U.S.A., ACM Press Series, 2001. ISBN: 0‐201‐59627‐X, 390 pages. Price: U.K. ?32.95, U.S.A. $49.99, soft cover.

Sustainability has emerged as a broad concern for society. Many engineering disciplines have been grappling with challenges in how we sustain technical, social and ecological systems. In the software engineering community, for example, maintainability has been a concern for a long time. But too often, these issues are treated in isolation from one another. Misperceptions among practitioners and research communities persist, rooted in a lack of coherent understanding of sustainability, and how it relates to software systems research and practice. This article presents a cross-disciplinary initiative to create a common ground and a point of reference for the global community of research and practice in software and sustainability, to be used for effectively communicating key issues, goals, values and principles of sustainability design for software-intensive systems. The centrepiece of this effort is the Karlskrona Manifesto for Sustainability Design, a vehicle for a much needed conversation about sustainability within and beyond the software community, and an articulation of the fundamental principles underpinning design choices that affect sustainability. We describe the motivation for developing this manifesto, including some considerations of the genre of the manifesto as well as the dynamics of its creation. We illustrate the collaborative reflective writing process and present the current edition of the manifesto itself. We assess immediate implications and applications of the articulated principles, compare these to current practice, and suggest future steps.

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Sustainability design and software: the karlskrona manifesto

Persistence - the storage and retrieval of application data from secondary storage media - is often used as a classical example of a crosscutting concern. It is widely assumed that an application can be developed without taking persistence requirements into consideration and a persistence aspect plugged in at a later stage. However, there are no real world examples showing whether persistence can in fact be aspectised and, if so, can this be done in a manner that promotes reuse and is oblivious to the application. In this paper, we provide an insight into these issues drawing upon our experience with a classical database application: a bibliography system. We argue that it is possible to aspectise persistence in a highly reusable fashion, which can be developed into a general aspect-based persistence framework. Nevertheless, application developers can only be partially oblivious to the persistent nature of the data. This is because persistence has to be accounted for as an architectural decision during the design of data-consumer components. Furthermore, designers of such components also need to consider the declarative nature of retrieval mechanisms supported by most database systems. Similarly, deletion requires explicit attention during application design as mostly applications trigger such an operation.

/pdf/persistence-as-an-aspect-33zxebcccw.pdf

Persistence as an aspect

Software's critical role in society demands a paradigm shift in the software engineering mind-set. This shift's focus begins in requirements engineering. This article is part of a special issue on the Future of Software Engineering.

https://www.zora.uzh.ch/id/eprint/125853/1/IEEESE2016.pdf

Requirements: The Key to Sustainability

Software Product Line Engineering (SPLE) requires the construction of feature models from large, unstructured and heterogeneous documents, and the reliable derivation of product variants from the resulting model. This can be an arduous task when performed manually, and can be error-prone in the presence of a change in requirements. In this paper we introduce a tool suite which automatically processes natural-language requirements documents into a candidate feature model, which can be refined by the requirements engineer. The framework also guides the process of identifying variant concerns and their composition with other features. We also provide language support for specifying semantic variant feature compositions which are resilient to change. We show that feature models produced by this framework compare favourably with those produced by domain experts by application to a real-life industrial example.

A framework for constructing semantically composable feature models from natural language requirements

In this paper, we discuss the limitations of the current syntactic composition mechanisms in aspect-oriented requirements engineering (AORE). We highlight that such composition mechanisms not only increase coupling between aspects and base concerns but are also insufficient to capture the intentionality of the aspect composition. Furthermore, they force the requirements engineer to reason about semantic influences and trade-offs among aspects from a syntactic perspective. We present a requirements description language (RDL) that enriches the existing natural language requirements specification with semantic information derived from the semantics of the natural language itself. Composition specifications are written based on these semantics rather than requirements syntax hence providing improved means for expressing the intentionality of the composition, in turn facilitating semantics-based reasoning about aspect influences and trade-offs. We also discuss the practicality of the use of this RDL by outlining the automation support for requirements annotation (realized as an extension of the Wmatrix natural language processing tool suite) to expose the semantics which are in turn utilized to facilitate composition and analysis (supported by the MRAT tool).

Ruzanna Chitchyan

Papers

Sustainability design and software: the karlskrona manifesto

Persistence as an aspect

Requirements: The Key to Sustainability

A framework for constructing semantically composable feature models from natural language requirements

Semantics-based composition for aspect-oriented requirements engineering