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

Software architectures shift the focus of developers from lines-of-code to coarser-grained architectural elements and their overall interconnection structure. Architecture description languages (ADLs) have been proposed as modeling notations to support architecture-based development. There is, however, little consensus in the research community on what is an ADL, what aspects of an architecture should be modeled in an ADL, and which of several possible ADLs is best suited for a particular problem. Furthermore, the distinction is rarely made between ADLs on one hand and formal specification, module interconnection, simulation and programming languages on the other. This paper attempts to provide an answer to these questions. It motivates and presents a definition and a classification framework for ADLs. The utility of the definition is demonstrated by using it to differentiate ADLs from other modeling notations. The framework is used to classify and compare several existing ADLs, enabling us, in the process, to identify key properties of ADLs. The comparison highlights areas where existing ADLs provide extensive support and those in which they are deficient, suggesting a research agenda for the future.

/pdf/a-classification-and-comparison-framework-for-software-3so7xapmcq.pdf

A classification and comparison framework for software architecture description languages

This paper presents an overview of the field of software systems requirements engineering (RE). It describes the main areas of RE practice, and highlights some key open research issues for the future.

/pdf/requirements-engineering-a-roadmap-3lcu5ikya8.pdf

Requirements engineering: a roadmap

From the Book:
“Clean code that works” is Ron Jeffries’ pithy phrase. The goal is clean code that works, and for a whole bunch of reasons:
Clean code that works is a predictable way to develop. You know when you are finished, without having to worry about a long bug trail.Clean code that works gives you a chance to learn all the lessons that the code has to teach you. If you only ever slap together the first thing you think of, you never have time to think of a second, better, thing. Clean code that works improves the lives of users of our software.Clean code that works lets your teammates count on you, and you on them.Writing clean code that works feels good.But how do you get to clean code that works? Many forces drive you away from clean code, and even code that works. Without taking too much counsel of our fears, here’s what we do—drive development with automated tests, a style of development called “Test-Driven Development” (TDD for short). 
In Test-Driven Development, you:
Write new code only if you first have a failing automated test.Eliminate duplication.

Two simple rules, but they generate complex individual and group behavior. Some of the technical implications are:You must design organically, with running code providing feedback between decisionsYou must write your own tests, since you can’t wait twenty times a day for someone else to write a testYour development environment must provide rapid response to small changesYour designs must consist of many highly cohesive, loosely coupled components, just to make testing easy

The two rules imply an order to the tasks ofprogramming:
1. Red—write a little test that doesn’t work, perhaps doesn’t even compile at first
2. Green—make the test work quickly, committing whatever sins necessary in the process
3. Refactor—eliminate all the duplication created in just getting the test to work


Red/green/refactor. The TDD’s mantra.

Assuming for the moment that such a style is possible, it might be possible to dramatically reduce the defect density of code and make the subject of work crystal clear to all involved. If so, writing only code demanded by failing tests also has social implications:
If the defect density can be reduced enough, QA can shift from reactive to pro-active workIf the number of nasty surprises can be reduced enough, project managers can estimate accurately enough to involve real customers in daily developmentIf the topics of technical conversations can be made clear enough, programmers can work in minute-by-minute collaboration instead of daily or weekly collaborationAgain, if the defect density can be reduced enough, we can have shippable software with new functionality every day, leading to new business relationships with customers


So, the concept is simple, but what’s my motivation? Why would a programmer take on the additional work of writing automated tests? Why would a programmer work in tiny little steps when their mind is capable of great soaring swoops of design? Courage.
Courage
Test-driven development is a way of managing fear during programming. I don’t mean fear in a bad way, pow widdle prwogwammew needs a pacifiew, but fear in the legitimate, this-is-a-hard-problem-and-I-can’t-see-the-end-from-the-beginning sense. If pain is nature’s way of saying “Stop!”, fear is nature’s way of saying “Be careful.” Being careful is good, but fear has a host of other effects:
Makes you tentativeMakes you want to communicate lessMakes you shy from feedbackMakes you grumpy

None of these effects are helpful when programming, especially when programming something hard. So, how can you face a difficult situation and:
Instead of being tentative, begin learning concretely as quickly as possible.Instead of clamming up, communicate more clearly.Instead of avoiding feedback, search out helpful, concrete feedback.(You’ll have to work on grumpiness on your own.) 

Imagine programming as turning a crank to pull a bucket of water from a well. When the bucket is small, a free-spinning crank is fine. When the bucket is big and full of water, you’re going to get tired before the bucket is all the way up. You need a ratchet mechanism to enable you to rest between bouts of cranking. The heavier the bucket, the closer the teeth need to be on the ratchet.

The tests in test-driven development are the teeth of the ratchet. Once you get one test working, you know it is working, now and forever. You are one step closer to having everything working than you were when the test was broken. Now get the next one working, and the next, and the next. By analogy, the tougher the programming problem, the less ground should be covered by each test.

Readers of Extreme Programming Explained will notice a difference in tone between XP and TDD. TDD isn’t an absolute like Extreme Programming. XP says, “Here are things you must be able to do to be prepared to evolve further.” TDD is a little fuzzier. TDD is an awareness of the gap between decision and feedback during programming, and techniques to control that gap. “What if I do a paper design for a week, then test-drive the code? Is that TDD?” Sure, it’s TDD. You were aware of the gap between decision and feedback and you controlled the gap deliberately.

That said, most people who learn TDD find their programming practice changed for good. “Test Infected” is the phrase Erich Gamma coined to describe this shift. You might find yourself writing more tests earlier, and working in smaller steps than you ever dreamed would be sensible. On the other hand, some programmers learn TDD and go back to their earlier practices, reserving TDD for special occasions when ordinary programming isn’t making progress.

There are certainly programming tasks that can’t be driven solely by tests (or at least, not yet). Security software and concurrency, for example, are two topics where TDD is not sufficient to mechanically demonstrate that the goals of the software have been met. Security relies on essentially defect-free code, true, but also on human judgement about the methods used to secure the software. Subtle concurrency problems can’t be reliably duplicated by running the code.

Once you are finished reading this book, you should be ready to:
Start simplyWrite automated testsRefactor to add design decisions one at a time

This book is organized into three sections.
An example of writing typical model code using TDD. The example is one I got from Ward Cunningham years ago, and have used many times since, multi-currency arithmetic. In it you will learn to write tests before code and grow a design organically.An example of testing more complicated logic, including reflection and exceptions, by developing a framework for automated testing. This example also serves to introduce you to the xUnit architecture that is at the heart of many programmer-oriented testing tools. In the second example you will learn to work in even smaller steps than in the first example, including the kind of self-referential hooha beloved of computer scientists.Patterns for TDD. Included are patterns for the deciding what tests to write, how to write tests using xUnit, and a greatest hits selection of the design patterns and refactorings used in the examples.

I wrote the examples imagining a pair programming session. If you like looking at the map before wandering around, you may want to go straight to the patterns in Section 3 and use the examples as illustrations. If you prefer just wandering around and then looking at the map to see where you’ve been, try reading the examples through and refering to the patterns when you want more detail about a technique, then using the patterns as a reference.

Several reviewers have commented they got the most out of the examples when they started up a programming environment and entered the code and ran the tests as they read.

A note about the examples. Both examples, multi-currency calculation and a testing framework, appear simple. There are (and I have seen) complicated, ugly, messy ways of solving the same problems. I could have chosen one of those complicated, ugly, messy solutions to give the book an air of “reality.” However, my goal, and I hope your goal, is to write clean code that works. Before teeing off on the examples as being too simple, spend 15 seconds imagining a programming world in which all code was this clear and direct, where there were no complicated solutions, only apparently complicated problems begging for careful thought. TDD is a practice that can help you lead yourself to exactly that careful thought.

Test Driven Development: By Example

Requirements are usually understood as stating what a system is supposed to do, as apposed to how it should do it. However, understanding the organizational context and rationales (the "Whys") that lead up to systems requirements can be just as important for the ongoing success of the system. Requirements modelling techniques can be used to help deal with the knowledge and reasoning needed in this earlier phase of requirements engineering. However most existing requirements techniques are intended more for the later phase of requirements engineering, which focuses on completeness, consistency, and automated verification of requirements. In contrast, the early phase aims to model and analyze stakeholder interests and how they might be addressed, or compromised, by various system-and-environment alternatives. This paper argues, therefore, that a different kind of modelling and reasoning support is needed for the early phase. An outline of the i* framework is given as an example of a step in this direction. Meeting scheduling is used as a domain example.

/pdf/towards-modelling-and-reasoning-support-for-early-phase-48szrigqxo.pdf

Towards modelling and reasoning support for early-phase requirements engineering

Time for change ... Alas, my term of office from January 2006 to December 2009 as the Editor in Chief of the IEEE Transactions on Software Engineering has ended. It has been a great honor to serve and I have very much enjoyed it. It provided me with an interesting overview of the field and an opportunity to make contact with the many researchers and practitioners who contribute to TSE. Despite the onerous responsibility—and it is hard work—I shall miss it. It is my great pleasure to introduce the new Editor in Chief, Professor Bashar Nuseibeh, who takes over from 1 January 2010. Bashar is a professor in computing at the Open University in the United Kingdom and Chief Scientist at Lero—the Irish Software Engineering Research Centre. He is a gifted researcher with an extensive portfolio of major research contributions. Bashar is a very active member of the software engineering community and a loyal supporter of the IEEE Transactions on Software Engineering, having served as an associate editor on the Editorial Board for the last four years. The journal is in excellent hands, and I am delighted that Bashar agreed to undertake this responsibility. State of the journal During my term of office, I have endeavored to attract more submissions, to improve the quality of the published papers, to widen their relevance and appeal, and to strengthen the archival nature of the journal. I am pleased to report that the journal is in a very healthy state, and the excellent reputation of TSE has continued to strengthen, in terms of both submissions and citations/impact. There has been a steady increase in submissions, starting from 302 papers in 2006, 356 in 2007, 412 in 2008, and finally 418 in 2009. This annual increase in submissions reflects an encouraging interest by authors in the selection of TSE as their journal of choice for publication. As a complement to the submissions figures, those for impact and citations reflect the growing reputation of TSE among readers as a good source of relevant results. Thomson reports that citations have risen dramatically from 3,165 in 2005, 3,203 in 2006, and 3,672 in 2007 to the latest figure of 5,449 for 2008. Impact factor is a measure of the frequency with which the “average article” in a journal has been cited in a particular year. The results for TSE impact have similarly risen from 1.967 in 2005, 2.132 in 2006, and 2.105 in 2007 to the latest figure of 3.569 for 2008. Thanks to the hard work and expertise of our Associate Editors and reviewers, the reviewing process remains efficient yet rigorous. The current acceptance rate for the journal is around 12 percent. The average time from submission to first decision is around three and half months, from submission to final decision is about seven and half months, from acceptance to electronic publication (Rapid Posting) just over a month, and from submission to paper publication is around 14 months. For a particular paper, the time from submission to a decision varies because of many factors, including the length of the manuscript, the workload of the editors involved, and the workloads of the various reviewers. Editorial Board On behalf of the software engineering community, I would like to express my sincere thanks to all of the Associate Editors who served as members of the Board during my term of office. The Editorial Board is the foundation upon which TSE is built. Board membership is both an honor and a duty. Despite many other commitments, these colleagues of international repute voluntarily give of their time and effort, taking responsibility for selecting reviewers, overseeing the reviewing process, and making the final recommendations regarding acceptability. They are instrumental in maintaining the reputation of the IEEE Transactions on Software Engineering as a first-class journal. I am very grateful for their unstinting support and expert advice; it has been a privilege to work with them. Like me, a number of Associate Editors reached the end of their terms of office in December 2009: my sincere thanks to Jo Atlee, Shing-Chi Cheung, Rance Cleaveland, Prem Devanbu, Susanna Donatelli, Matt Dwyer, Wolfgang Emmerich, Mark Harman, Audris Mockus, Hausi Muller, Bashar Nuseibeh, Wilhelm Schaefer, Neeraj Suri, Dick Taylor, Sebastian Uchitel, and AlexWolf.

Editorial: A New Editor in Chief and the State of the Journal

In recent years, research efforts have been directed towards the use of Machine Learning (ML) techniques to support and automate activities such as program repair, specification mining and risk assessment. The focus has largely been on techniques for classification, clustering and regression. Although beneficial, these do not produce a declarative, interpretable representation of the learned information. Hence, they cannot readily be used to inform, revise and elaborate software models. On the other hand, recent advances in ML have witnessed the emergence of new logic-based learning approaches that differ from traditional ML in that their output is represented in a declarative, rule-based manner, making them well-suited for many software engineering tasks. In this technical briefing, we will introduce the audience to the latest advances in logic-based learning, give an overview of how logic-based learning systems can successfully provide automated support to a variety of software engineering tasks, demonstrate the application to two real case studies from the domain of requirements engineering and software design and highlight future challenges and directions.

/pdf/logic-based-learning-in-software-engineering-2jrgppdbae.pdf

Logic-based learning in software engineering

Directions in software process modelling and technology

This paper outlines a framework for “CASE tool” development and integration which supports distribution.

/pdf/a-viewpoint-based-framework-for-software-development-1pq7qd7ndo.pdf

A viewpoint-based framework for software development environments

This work describes a new approach for behaviour model extraction which combines static and dynamic information. We exploit context information as a way of merging these types of information. Contexts are defined by evaluated control predicates and values of attributes. They create a nested structure that can facilitate the extraction of causal relations between system actions. We show how context information can guide the process of constructing LTS models that are good approximations of the actual behaviour of the systems they describe. These models can be used for automated analysis and property verification. Augmentation of the values of attributes recorded in contexts produces further refined models and leads towards correct models. Completeness of the extracted models depends on the coverage achieved by samples of executions. Our approach is partially automated by a tool called LTSE. Results of one of our case studies are presented and discussed.

Jeff Kramer

Papers

Editorial: A New Editor in Chief and the State of the Journal

Logic-based learning in software engineering

Directions in software process modelling and technology

A viewpoint-based framework for software development environments

Model extraction using context information