@article {16297,
	title = {An experiment to assess the cost-benefits of code inspections in large scale software development},
	journal = {IEEE Transactions on Software Engineering},
	volume = {23},
	year = {1997},
	month = {1997/06//},
	pages = {329 - 346},
	abstract = {We conducted a long term experiment to compare the costs and benefits of several different software inspection methods. These methods were applied by professional developers to a commercial software product they were creating. Because the laboratory for this experiment was a live development effort, we took special care to minimize cost and risk to the project, while maximizing our ability to gather useful data. The article has several goals: (1) to describe the experiment{\textquoteright}s design and show how we used simulation techniques to optimize it; (2) to present our results and discuss their implications for both software practitioners and researchers; and (3) to discuss several new questions raised by our findings. For each inspection, we randomly assigned three independent variables: (1) the number of reviewers on each inspection team (1, 2, or 4); (2) the number of teams inspecting the code unit (1 or 2); and (3) the requirement that defects be repaired between the first and second team{\textquoteright}s inspections. The reviewers for each inspection were randomly selected without replacement from a pool of 11 experienced software developers. The dependent variables for each inspection included inspection interval (elapsed time), total effort, and the defect detection rate. Our results showed that these treatments did not significantly influence the defect detection effectiveness, but that certain combinations of changes dramatically increased the inspection interval},
	keywords = {Analysis of variance, code inspection cost benefits, code unit, commercial software product, Computer Society, cost-benefit analysis, Costs, defect detection effectiveness, defect detection rate, Design optimization, experienced software developers, experiment design, independent variables, Inspection, inspection interval, inspection team, Laboratories, large scale software development, Large-scale systems, live development effort, long term experiment, professional aspects, professional developers, Programming, reviewers, simulation techniques, software cost estimation, software inspection methods, software practitioners, Software quality, Switches},
	isbn = {0098-5589},
	doi = {10.1109/32.601071},
	author = {Porter, Adam and Siy,H. P and Toman,C. A and Votta,L. G.}
}
@article {16323,
	title = {Learning from examples: generation and evaluation of decision trees for software resource analysis},
	journal = {IEEE Transactions on Software Engineering},
	volume = {14},
	year = {1988},
	month = {1988/12//},
	pages = {1743 - 1757},
	abstract = {A general solution method for the automatic generation of decision (or classification) trees is investigated. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for one problem domain, specifically, that of software resource data analysis. The purpose of the decision trees is to identify classes of objects (software modules) that had high development effort, i.e. in the uppermost quartile relative to past data. Sixteen software systems ranging from 3000 to 112000 source lines have been selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4700 objects, capture a multitude of information about the objects: development effort, faults, changes, design style, and implementation style. A total of 9600 decision trees are automatically generated and evaluated. The analysis focuses on the characterization and evaluation of decision tree accuracy, complexity, and composition. The decision trees correctly identified 79.3\% of the software modules that had high development effort or faults, on the average across all 9600 trees. The decision trees generated from the best parameter combinations correctly identified 88.4\% of the modules on the average. Visualization of the results is emphasized, and sample decision trees are included},
	keywords = {Analysis of variance, Artificial intelligence, Classification tree analysis, Data analysis, decision theory, Decision trees, Fault diagnosis, Information analysis, machine learning, metrics, NASA, production environment, software engineering, software modules, software resource analysis, Software systems, Termination of employment, trees (mathematics)},
	isbn = {0098-5589},
	doi = {10.1109/32.9061},
	author = {Selby,R. W and Porter, Adam}
}
