@article{kitchenham07,
        title = {Misleading Metrics and Unsound Analyses},
        author = {Barbara Kitchenham and David Ross Jeffery and Colin Connaughton},
        journal = {IEEE Software},
        month = {Mar/Apr},
        number = 2,
        pages = {73--78},
        volume = 24,
        year = 2007,
        url = {http://doi.ieeecomputersociety.org/10.1109/MS.2007.49},
        abstract = {The authors demonstrate that the recommendations for analyzing productivity in the appendix to the ISO/IEC 15939 standard are inappropriate. They also show that problems with the ISO/IEC advice can be compounded if software engineers attempt to apply statistical process-control techniques to software productivity metrics. They recommend using small meaningful data sets as the basis for productivity analysis and using effort-estimation models to assess productivity rather than productivity metrics.This article is part of a special focus section on software metrics.},
	biburl = {http://www.bibsonomy.org/bibtex/24c8126face6568c6ad513aea8e42e970/neilernst},
	keywords = {software metrics requirements}
}

@article{andersson90,
        title = {A {S}urvey on {S}oftware {Q}uality {M}etrics},
        author = {Thorbjorn Andersson},
        journal = {?},
        year = 1990,
        url = {http://www.cs.toronto.edu/~nernst/papers/andersson90surveyqualitymetrics.pdf},
        id = {111735},
	priority = {3},
	pdf = {andersson90surveyqualitymetrics.pdf},
	description = {sdasda},
	abstract = {This is a survey of software quality metrics. Definitions of the terms quality and software quality found in the literature are presented. A note on the difference between the use of software metrics and software quality assurance is given. A classification of software quality metrics is presented. The classification is: classical, product and process metrics. A number of different metrics relating to maintenance are described. This will be followed by brief discussions about automatic collection of software metrics data, use of collected data, and costs of software metrics.},
	biburl = {http://www.bibsonomy.org/bibtex/2913991804d1cf2043e4efffdd7d9dba1/neilernst},
	keywords = {software metrics engineering}
}

@inproceedings{subramanian03,
        title = {Process-oriented metrics for software architecture evolvability},
        author = {Nary Subramanian and L. Chung},
        booktitle = {International Workshop on Principles of Software Evolution},
        journal = {Software Evolution, 2003. Proceedings. Sixth International Workshop
	on Principles of},
        pages = {65--70},
        year = 2003,
        url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1231212},
        id = {638225},
	priority = {3},
	description = {Not previously uploaded},
	abstract = {Evolution of software systems is almost a natural process. Evolution
	can occur at different levels of abstraction of software. Evolution
	at the architectural level, being the highest level of solution,
	can often times be the most critical to the success and survival
	of the pertaining software system. Metrics for software architectural
	evolvability will help determine the extent to which the architectural
	evolution can take place. We propose a framework called the POMSAE,
	process-oriented metrics for software architecture evolvability,
	that will help not only to intuitively develop architectural evolvability
	metrics but also to trace the metrics back to the evolvability requirements.
	This will then help analyze the reasons for the strengths/weaknesses
	in the metrics. POMSAE is partially validated by demonstrating its
	application to two practical telecom systems.},
	biburl = {http://www.bibsonomy.org/bibtex/210b391d67af8051091333a159a6c3faa/neilernst},
	keywords = {process metrics architecture evolution}
}

@inproceedings{rajaraman92,
        title = {Reliability and maintainability related software coupling metrics
	in C++ programs},
        author = {C. Rajaraman and M. R. Lyu},
        booktitle = {Intl Symp. on Software Reliability Engineering},
        journal = {, 1992. Proceedings., Third International Symposium on},
        pages = {303--311},
        year = 1992,
        url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=285898},
        id = {671581},
	priority = {0},
	description = {Not previously uploaded},
	abstract = {A description is given of some difficulties that one encounters in
	the testing and maintenance of C++ programs, which may result in
	program unreliability. Inheritance and polymorphism are key concepts
	in object-oriented programming (OOP), and are essential for achieving
	reusability and extendibility, but they also make programs more
	difficult to understand. The authors show by arguments and by some
	empirical evidence that widely used complexity metrics like lines
	of code, cyclomatic complexity, and Software Science's metrics may
	not be appropriate to measure the complexity of C++ programs and
	those written in other object-oriented languages, since they do
	not address concepts like inheritance and encapsulation, apart from
	having other weaknesses. Some measures using a notion from the world
	of functional decomposition-coupling, are defined for C++ programs.
	Two of them-CC and AMC-and equivalent ones for the three widely
	used complexity metrics (for comparison) are computed for five C++
	programs. Preliminary results show that the coupling measures correlate
	better with difficulty of testing and maintenance than the three
	widely used complexity metrics},
	biburl = {http://www.bibsonomy.org/bibtex/2975b4b74d46c520307044eb0026d6cd3/neilernst},
	keywords = {software metrics maintenance}
}

@article{avotins94,
        title = {Defining and {D}esigning a {Q}uality {OO} {M}etrics {S}uite},
        author = {Jon Avotins},
        year = 1994,
        id = {111738},
	priority = {2},
	pdf = {avotins94definingoometrics.pdf},
	description = {sdasda},
	biburl = {http://www.bibsonomy.org/bibtex/2c44f460848d6c9c2dc5c06c2f1e25e0b/neilernst},
	keywords = {software metrics engineering}
}

@mastersthesis{yu01,
        title = {Empirical validation of an object-oriented metrics suite: A case study},
        address = {Victoria},
        author = {Ping Yu},
        booktitle = {Computer Science},
        howpublished = {Unpublished},
        publisher = {University of Victoria},
        school = {University of Victoria},
        year = 2001,
        id = {120109},
	priority = {1},
	comment = {on paper copy... and in thesis},
	description = {sdasda},
	biburl = {http://www.bibsonomy.org/bibtex/260d74293d826aaf4655437898d989340/neilernst},
	keywords = {software metrics engineering}
}

@inproceedings{systa00,
        title = {Analyzing {J}ava {S}oftware by {C}ombining {M}etrics and {P}rogram {V}isualization},
        address = {Italy?},
        author = {Tarja Systä and Ping Yu and Hausi Müller},
        booktitle = {International Conference on Software Maintenance and Reengineering},
        pages = {199--209},
        year = 2000,
        url = {http://www.cs.toronto.edu/~nernst/papers/ping-metrics-reengineering.pdf},
        id = {121528},
	priority = {0},
	comment = {Simple approach to analysing software with some new metrics. Metrics research is not exactly earth-shattering.},
	description = {sdasda},
	biburl = {http://www.bibsonomy.org/bibtex/229d1b83ce4f3e8e0211495339c164873/neilernst},
	keywords = {software metrics java engineering}
}

@article{purchase02,
        title = {Metrics for Graph Drawing Aesthetics},
        author = {Helen C. Purchase},
        journal = {J. Visual Lang. and Comp.},
        number = 5,
        pages = {501--516},
        volume = 13,
        year = 2002,
        url = {http://www.cs.toronto.edu/~nernst/papers/viz-hci\purchase02-jvlc.pdf},
        id = {121598},
	priority = {0},
	doi = {10.1006/S1045 -926X(02)00016-2},
	description = {sdasda},
	biburl = {http://www.bibsonomy.org/bibtex/237f77b371ad7ba93fee0093af38a5386/neilernst},
	keywords = {visualization graph metrics}
}

@inproceedings{legrand01a,
        title = {Topic {M}aps {M}etrics and {V}isualization},
        address = {Austin Texas},
        author = {Benedicte Legrand},
        booktitle = {Knowledge Technologies},
        year = 2001,
        url = {http://www.cs.toronto.edu/~nernst/papers/LeGrand-topic-maps-metrics-ppt.pdf},
        id = {121760},
	priority = {0},
	comment = {-present topics using city metaphor and 3d model - other papers exist but I can't remember where they are - some interesting metrics I think but haven't analyzed them,},
	description = {sdasda},
	biburl = {http://www.bibsonomy.org/bibtex/25339c4c70fad5b8a27b186931e0a94a4/neilernst},
	keywords = {topic visualization metrics maps}
}

@article{lethbridge97,
        title = {Metrics for {C}oncept-{O}riented {K}nowledge {B}ases},
        author = {Timothy C. Lethbridge},
        journal = {Int. J. of Software Engineering \& Knowledge Engineering},
        year = 1997,
        url = {http://www.cs.toronto.edu/~nernst/papers/lethbridge-Metrics.pdf},
        id = {121764},
	priority = {2},
	description = {sdasda},
	abstract = {Metrics are widely researched and used in software engineering; however there is little analogous work in the field of knowledge engineering. In other words, there are no widely-known metrics that the developers of knowledge bases can use to monitor and improve their work. In this paper we adapt the GQM (Goals-Questions-Metrics ) methodology that is used to select and develop software metrics. We use the methodology to develop a series of metrics that measure the size and complexity of concept-oriented knowledge bases. Two of the metrics measure raw size; seven measure various aspects of complexity on scales of 0 to 1, and are shown to be largely independent of each other. The remaining three are compound metrics that combine aspects of the other nine in an attempt to measure the overall ?difficulty? or ?complexity? of a knowledge base. The metrics have been implemented and tested in the context of a knowledge management system called CODE4.},
	biburl = {http://www.bibsonomy.org/bibtex/2e65ddf0778ff63fcebca3ecbdef87b82/neilernst},
	keywords = {metrics ontology}
}

@inproceedings{hou02,
        title = {A {T}emplate-based approach toward acquisition of logical sentences},
        author = {C. S. J. Hou and Natalya F. Noy and Mark A. Musen},
        booktitle = {2002 IIP Conf. WCC P},
        year = 2002,
        id = {121863},
	priority = {0},
	comment = {presents a result showing that in ontologies studied, 85% of constraints on the KB can be found in 20 templates or patterns, which suggests there is a way to automate this extra knowledge in a manner easier than entering complex FOL statements.},
	description = {sdasda},
	biburl = {http://www.bibsonomy.org/bibtex/248129d12fd9830dcde647899de2dd44c/neilernst},
	keywords = {acquisition knowledge metrics ontology}
}

@article{mccabe89,
        title = {Design complexity measurement and testing},
        author = {Thomas J. Mccabe and Charles W. Butler},
        journal = {Communications of the ACM},
        month = {December},
        number = 12,
        pages = {1415--1425},
        publisher = {ACM Press},
        volume = 32,
        year = 1989,
        url = {http://dx.doi.org/10.1145/76380.76382},
        id = {126098},
	issn = {0001-0782},
	priority = {2},
	doi = {10.1145/76380.76382},
	description = {sdasda},
	biburl = {http://www.bibsonomy.org/bibtex/2dbbe7d3fa92336e80848b15cd4fde17f/neilernst},
	keywords = {software metrics engineering}
}

@book{boehm00,
        title = {Software Cost Estimation with Cocomo II (with CD-ROM)},
        address = {New Jersey},
        author = {Barry W. Boehm and Ellis Horowitz and Ray Madachy and Donald Reifer and Bradford K. Clark and Bert Steece and Winsor A. Brown and Sunita Chulani and Chris Abts},
        howpublished = {Hardcover},
        month = {January},
        publisher = {Prentice Hall PTR},
        year = 2000,
        url = {http://www.amazon.fr/exec/obidos/ASIN/0130266922/citeulike04-21},
        id = {341462},
	priority = {1},
	isbn = {0130266922},
	description = {sdasda},
	biburl = {http://www.bibsonomy.org/bibtex/2bd4488deb8b3d0489a37544b4f0dbd1c/neilernst},
	keywords = {software metrics estimation}
}

@article{gyimothy05,
        title = {Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction},
        author = {T. Gyimothy and R. Ferenc and I. Siket},
        journal = {Software Engineering, IEEE Transactions on},
        number = 10,
        pages = {897--910},
        volume = 31,
        year = 2005,
        url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1542070},
        id = {430826},
	priority = {2},
	description = {sdasda},
	abstract = {Open source software systems are becoming increasingly important these days. Many companies are investing in open source projects and lots of them are also using such software in their own work. But, because open source software is often developed with a different management style than the industrial ones, the quality and reliability of the code needs to be studied. Hence, the characteristics of the source code of these projects need to be measured to obtain more information about it. This paper describes how we calculated the object-oriented metrics given by Chidamber and Kemerer to illustrate how fault-proneness detection of the source code of the open source Web and e-mail suite called Mozilla can be carried out. We checked the values obtained against the number of bugs found in its bug database—called Bugzilla—using regression and machine learning methods to validate the usefulness of these metrics for fault-proneness prediction. We also compared the metrics of several versions of Mozilla to see how the predicted fault-proneness of the software system changed during its development cycle.},
	biburl = {http://www.bibsonomy.org/bibtex/25fc71d62f324b2335f3c74317e0f5257/neilernst},
	keywords = {software metrics empirical}
}