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Empirical Analysis of Object-Oriented Design Metrics for Predicting Unit Testing Effort of Classes

Journal of software engineering and Applications 2012年第7期5卷 513-526页

作者： Mourad Badri Fadel Toure Software Engineering Research Laboratory Department of Mathematics and Computer Science University of Quebec Trois- Rivières Canada Software Engineering Research Laboratory Department of Mathematics and Computer Science University of Quebec Trois-Rivières Canada

In this paper, we investigate empirically the relationship between object-oriented design metrics and testability of classes. We address testability from the point of view of unit testing effort. We collected data from three open source Java software systems for which JUnit test cases exist. To capture the testing effort of classes, we used metrics to quantify the corresponding JUnit test cases. Classes were classified, according to the required unit testing effort, in two categories: high and low. In order to evaluate the relationship between object-oriented design metrics and unit testing effort of classes, we used logistic regression methods. We used the univariate logistic regression analysis to evaluate the individual effect of each metric on the unit testing effort of classes. The multivariate logistic regression analysis was used to explore the combined effect of the metrics. The performance of the prediction models was evaluated using Receiver Operating Characteristic analysis. The results indicate that: 1) complexity, size, cohesion and (to some extent) coupling were found significant predictors of the unit testing effort of classes and 2) multivariate regression models based on object-oriented design metrics are able to accurately predict the unit testing effort of classes.

关键词： software Testability Unit Testing Testing Effort Metrics Relationship Prediction and Empirical Analysis

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Managing implicit requirements using semantic case-based reasoning research preview

Managing implicit requirements using semantic case-based rea...

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18th Working Conference on Requirements engineering: Foundation for software Quality, REFSQ 2012

作者： Daramola, Olawande Moser, Thomas Sindre, Guttorm Biffl, Stefan Department of Computer and Information Science Norwegian University of Science and Technology Trondheim Norway Christian Doppler Laboratory for Software Engineering Integration for Flexible Automation Systems Vienna University of Technology Austria

ISBN: (纸本)9783642287138

[Context and motivation] Implicit requirements (ImRs) are defined as requirements of a system which are not explicitly expressed during requirements elicitation, often because they are considered so basic that developers should already know them. Many products have been rejected or users made unhappy because implicit requirements were not sufficiently addressed. [Question/Problem] Requirement management tools have not addressed the issue of managing ImRs, also despite the challenges of managing ImRs that exist in practice the issue has not received sufficient attention in the literature. [Principal Idea/results] This planned research will investigate how automated support can be provided for managing ImRs within an organizational context, which is currently lacking in practice. This work proposed an approach that is based on semantic case-based reasoning for managing ImRs. [Contribution] We present the concept of a tool which enables managing of ImRs through the analogy-based requirements reuse of previously known ImRs. This ensures the discovery, structured documentation, proper prioritization, and evolution of ImRs, which improves the overall success of software development processes. © 2012 Springer-Verlag.

关键词： Case based reasoning

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Reports of the AAAI 2011 fall symposia

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AI Magazine 2012年第1期33卷 71-78页

作者： Blisard, Sam Carmichael, Ted Ding, Li Finin, Tim Frost, Wende Graesser, Arthur Hadzikadic, Mirsad Kagal, Lalana Kruijff, Geert-Jan M. Langley, Pat Lester, James McGuinness, Deborah L. Mostow, Jack Papadakis, Panagiotis Pirri, Fiora Prasad, Rashmi Stoyanchev, Svetlana Varakantham, Pradeep Interactive Systems Section Naval Research Laboratory Washington DC United States Department of Software and Information Systems University of North Carolina Charlotte United States Qualcomm Inc. United States Department of Computer Science and Electrical Engineering University of Maryland Baltimore County United States Department of Psychology Institute of Intelligent Systems University of Memphis United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Germany Department of Computer Science and Engineering Arizona State University Tempe AZ United States Department of Computer Science North Carolina State University United States Department of Computer Science and Cognitive Science Rensselaer Polytechnic Institute United States School of Computer Science Carnegie Mellon University United States Department of Informatics and Systems University of Sapienza Rome Italy Department of Informatics and Systems ALCOR Lab University of Sapienza Rome Italy Department of Health Informatics and Administration University of Wisconsin-Milwaukee United States Columbia University United States School of Information Systems Singapore Management University Singapore Singapore

The Association for the Advancement of Artificial Intelligence was pleased to present the 2011 Fall Symposium Series, held Friday through Sunday, November 4-6, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the seven symposia are as follows: (1) Advances in Cognitive systems;(2) Building Representations of Common Ground with Intelligent Agents;(3) Complex Adaptive systems: Energy, Information, and Intelligence;(4) Multiagent Coordination under Uncertainty;(5) Open Government Knowledge: AI Opportunities and Challenges;(6) Question Generation;and (7) Robot-Human Teamwork in Dynamic Adverse Environments. The highlights of each symposium are presented in this report. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Intelligent agents

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Editorial

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International Journal of Bio-Inspired Computation 2013年第4期5卷 197-198页

作者： Camacho, David Badica, Costin Bein, Doina Warnier, Martijn Computer Science Department Escuela Politecnica Superior Universidad Autonoma de Madrid C/Francisco Tomás y Valiente 28049 Madrid Spain Software Engineering Department Faculty of Automatics Computers and Electronics University of Craiova Bvd. Decebal 107 200440 Craiova Romania Applied Research Laboratory Information Science and Technology Division Pennsylvania State University 204 V Applied Science Building University Park PA 16802 United States Systems Engineering Section Faculty of Technology Policy and Management Delft University of Technology Jaffalaan 5 2628BX Delft Netherlands

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Modeling the affective states of students using SQL-tutor

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11th International Conference on Intelligent Tutoring systems, ITS 2012

作者： Guia, Thea Faye G. Rodrigo, Ma. Mercedes T. Dagami, Michelle Marie C. Sugay, Jessica O. Macam, Francis Jan P. Mitrovic, Antonija Ateneo Laboratory for the Learning Sciences Department of Information Systems and Computer Science Ateneo de Manila University Loyola Heights Quezon City Philippines Department of Computer Science and Software Engineering University of Canterbury Private Bag 4800 Christchurch New Zealand

We attempted to build models of affect of students using SQL-Tutor. Most exhibited states are engaged concentration, confusion and boredom. Though none correlated with achievement, boredom and frustration persisted. U... 详细信息

ISBN: (纸本)9783642309496

关键词： Artificial intelligence

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Improving Class Cohesion Measurement: Towards a Novel Approach Using Hierarchical Clustering

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Journal of software engineering and Applications 2012年第7期5卷 449-458页

作者： Lazhar Sadaoui Mourad Badri Linda Badri Software Engineering Research Laboratory Department of Mathematics and Computer Science University of Quebec Trois-Rivières Canada

Class cohesion is considered as one of the most important object-oriented software attributes. High cohesion is, in fact, a desirable property of software. Many different metrics have been suggested in the last several years to measure the cohesion of classes in object-oriented systems. The class of structural object-oriented cohesion metrics is the most in-vestigated category of cohesion metrics. These metrics measure cohesion on structural information extracted from the source code. Empirical studies noted that these metrics fail in many situations to properly reflect cohesion of classes. This paper aims at exploring the use of hierarchical clustering techniques to improve the measurement of cohesion of classes in object-oriented systems. The proposed approach has been evaluated using three particular case studies. We also used in our study three well-known structural cohesion metrics. The achieved results show that the new approach appears to better reflect the cohesion (and structure) of classes than traditional structural cohesion metrics.

关键词： Object-Oriented Classes Cohesion Similarity Clustering Metrics and Empirical Evaluation

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Formation learning algorithms for mobile agents subject to 2-D dynamically changing topologies

Formation learning algorithms for mobile agents subject to 2...

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American Control Conference

作者： Deyuan Meng Yingmin Jia Junping Du Jun Zhang Wenling Li Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Beijing 100191 P. R. China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 P. R. China School of Electronic and Information Engineering Beihang University (BUAA) Beijing 100191 P. R. China

ISBN: (纸本)9781479901777

In this paper, we consider a two-dimensional (2-D) formation problem for multi-agent systems subject to switching topologies that dynamically change along both a finite time axis and an infinite iteration axis. We present a distributed iterative learning control (ILC) algorithm via the nearest neighbor rules. By employing the 2-D approach, we develop both the asymptotic and exponentially fast convergence of our formation ILC, which can be guaranteed by conditions in terms of the spectral radius and the matrix norms, respectively.

关键词： Multi-agent systems ILC switching topologies formation learning algorithms learning algorithms Multi-agent systems International Labour Conference Mobile agents spectral radius

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High availability through output continuity

High availability through output continuity

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2012 IEEE International Conference on Cluster Computing Workshops, Cluster Workshops 2012

作者： Ye, Wei Dong, Yaozu Ma, Ruhui Liang, Alei Guan, Haibing Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Intel China Software Center Shanghai 200241 China

ISBN: (纸本)9780768548449

Virtual machine (VM) based state machine approaches, i.e. VM replication, provide high availability without source code modifications, unfortunately, existing VM replication approaches suffer from excessive replication overheads. On the other hand, machine state replication is an overly strong (and therefore less efficient) requirement for high availability in the networked client-server system. In this paper, we propose a generic and highly efficient high availability solution through output continuity. Output continuity considers a backup server as a valid replica as long as the responses generated so far are the same between the backup and primary servers, the server state is propagated from the primary server to the backup server if and only if the previous outputs from the backup and primary servers are different. © 2012 IEEE.

关键词： Fault tolerance

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Constraint programming for path planning with uncertainty: Solving the optimal search path problem

Constraint programming for path planning with uncertainty: S...

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18th International Conference on Principles and Practice of Constraint Programming, CP 2012

作者： Morin, Michael Papillon, Anika-Pascale Abi-Zeid, Irène Laviolette, François Quimper, Claude-Guy Department of Computer Science and Software Engineering Université Laval Québec QC Canada Department of Mathematics and Statistics Université Laval Québec QC Canada Department of Operations and Decision Systems Université Laval Québec QC Canada

ISBN: (纸本)9783642335570

The optimal search path (OSP) problem is a single-sided detection search problem where the location and the detectability of a moving object are uncertain. A solution to this -hard problem is a path on a graph that maximizes the probability of finding an object that moves according to a known motion model. We developed constraint programming models to solve this probabilistic path planning problem for a single indivisible searcher. These models include a simple but powerful branching heuristic as well as strong filtering constraints. We present our experimentation and compare our results with existing results in the literature. The OSP problem is particularly interesting in that it generalizes to various probabilistic search problems such as intruder detection, malicious code identification, search and rescue, and surveillance. © 2012 Springer-Verlag.

关键词： Motion planning

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Convex and scalable weakly labeled SVMs

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Yu-Feng Li Ivor W. Tsang James T. Kwok Zhi-Hua Zhou Google MPI for Intelligent Systems National Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Engineering Nanyang Technological University Singapore Department of Computer Science and Engineering Hong Kong University of Science & Technology Hong Kong

In this paper, we study the problem of learning from weakly labeled data, where labels of the training examples are incomplete. This includes, for example, (i) semi-supervised learning where labels are partially known; (ii) multi-instance learning where labels are implicitly known; and (iii) clustering where labels are completely unknown. Unlike supervised learning, learning with weak labels involves a difficult Mixed-Integer Programming (MIP) problem. Therefore, it can suffer from poor scalability and may also get stuck in local minimum. In this paper, we focus on SVMs and propose the WELLSVM via a novel label generation strategy. This leads to a convex relaxation of the original MIP, which is at least as tight as existing convex Semi-Definite Programming (SDP) relaxations. Moreover, the WELLSVM can be solved via a sequence of SVM subproblems that are much more scalable than previous convex SDP relaxations. Experiments on three weakly labeled learning tasks, namely, (i) semi-supervised learning; (ii) multi-instance learning for locating regions of interest in content-based information retrieval; and (iii) clustering, clearly demonstrate improved performance, and WELLSVM is also readily applicable on large data sets.

关键词： clustering convex relaxation cutting plane multi-instance learning semi-supervised learning weakly labeled data

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