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

Background: The accurate prediction of where faults are likely to occur in code can help direct test effort, reduce costs, and improve the quality of software. Objective: We investigate how the context of models, the independent variables used, and the modeling techniques applied influence the performance of fault prediction models. Method: We used a systematic literature review to identify 208 fault prediction studies published from January 2000 to December 2010. We synthesize the quantitative and qualitative results of 36 studies which report sufficient contextual and methodological information according to the criteria we develop and apply. Results: The models that perform well tend to be based on simple modeling techniques such as Naive Bayes or Logistic Regression. Combinations of independent variables have been used by models that perform well. Feature selection has been applied to these combinations when models are performing particularly well. Conclusion: The methodology used to build models seems to be influential to predictive performance. Although there are a set of fault prediction studies in which confidence is possible, more studies are needed that use a reliable methodology and which report their context, methodology, and performance comprehensively.

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A Systematic Literature Review on Fault Prediction Performance in Software Engineering

by J. Biggs and C. Tang, Maidenhead, England,
Open University Press, 2007, 360 pp., £29.99, ISBN-13: 978-0-335-22126-4

Teaching for quality learning at university

Conditioned Reflexes; an Investigation of the Physiological Activity of the Cerebral Cortex.

Software engineering: theory and practice

At present, automated data collection tools allow us to collect large amounts of information, not without associated problems. This paper, we apply feature selection to several software engineering databases selecting attributes with the final aim that project managers can have a better global vision of the data they manage. In this paper, we make use of attribute selection techniques in different datasets publicly available (PROMISE repository), and different data mining algorithms for classification to defect faulty modules. The results show that in general, smaller datasets with less attributes maintain or improve the prediction capability with less attributes than the original datasets.

/pdf/detecting-fault-modules-applying-feature-selection-to-199gq50aay.pdf

Detecting Fault Modules Applying Feature Selection to Classifiers

Software Process and Product Measurement

Current e-learning systems are increasing their importance in higher education. However, the state of the art of e-learning applications, besides the state of the practice, does not achieve the level of interactivity that current learning theories advocate. In this paper, the possibility of enhancing e-learning systems to achieve deep learning has been studied by replicating an experiment in which students had to learn basic software engineering principles. One group learned these principles using a static approach, while the other group learned the same principles using a system-dynamics-based approach, which provided interactivity and feedback. The results show that, quantitatively, the latter group achieved a better understanding of the principles; furthermore, qualitatively, they enjoyed the learning experience

/pdf/e-learning-in-project-management-using-simulation-models-a-3yshtpq057.pdf

e-Learning in Project Management Using Simulation Models: A Case Study Based on the Replication of an Experiment

This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Software Measurement, IWSM-Mensura 2007, held in Palma de Mallorca, Spain, in November 2007. The 16 revised full papers presented were carefully reviewed and selected for inclusion in the book. The papers deal with aspects of software measurement like function-points measurement, effort and cost estimates, prediction, industrial experiences in software measurement, planning and implementing measurement, measurement-based software process improvement, best practices in software measurement, usability and user interaction measurement, measurement of open source projects, teaching and learning software measurement as well as new trends and ontologies for software measurement.

Software Process and Product Measurement: International Conference, IWSM-Mensura 2007, Palma de Mallorca, Spain, November 5-8, 2007. Revised Papers

The adoption of functional size measurement (FSM) methods in software organizations is growing. In particular, special attention is being paid to the COSMIC method, because of its novelties against 1st generation FSM methods such as IFPUG FPA. One of the main problems facing organizations wanting to use COSMIC is how to properly convert the software functional size of the projects in their portfolio measured by the previously adopted FSM method to the size measured by the new method. The objective of this paper is to find a sound mathematical basis for converting an IFPUG measurement to a COSMIC measurement. In the light of previously published researches, parallel measurements were performed to establish three new datasets (respectively composed by 21, 14 and 35 data points) and verified by an expert measurer, certified on both techniques. In order to obtain a more precise solution, the search for a mathematical relationship has been run using new nonlinear equation types. Results from the analysis confirmed an approximated conversion factor of 1:1, within a range between 0.9 and 1.1, but moving from a larger number of data points analyzed then in past studies. These results can be very useful for those companies starting to use their benchmarking databases populated in IFPUG FP units to projects measured in COSMIC FP.

Juan J. Cuadrado-Gallego

Papers

Detecting Fault Modules Applying Feature Selection to Classifiers

Software Process and Product Measurement

e-Learning in Project Management Using Simulation Models: A Case Study Based on the Replication of an Experiment

Software Process and Product Measurement: International Conference, IWSM-Mensura 2007, Palma de Mallorca, Spain, November 5-8, 2007. Revised Papers

An experimental study on the conversion between IFPUG and COSMIC functional size measurement units