The aim of the experiment presented in this paper is to present an insight into the evaluation of the potential benefits of introducing a function clone detection technology in an industrial software development process. To take advantage of function clone detection, two modifications to the software development process are presented. Our experiment consists of evaluating the impact that these proposed changes would have had on a specific software system if they had been applied over a 3 year period (involving 10000 person-months), where 6 subsequent versions of the software under study were released. The software under study is a large telecommunication system. In total 89 million lines of code have been analyzed. A first result showed that, against our expectations, a significant number of clones are being removed from the system over time. However, this effort is insufficient to prevent the growth of the overall number of clones in the system. In this context the first process change would have added value. We have also found that the second process change would have provided programmers with a significant number of opportunities for correcting problems before customers experienced them. This result shows a potential for improving the software system quality and customer satisfaction

This paper presents a technique to automatically identify duplicate and near duplicate functions in a large software system. The identification technique is based on metrics extracted from the source code using the tool Datrix/sup TM/. This clone identification technique uses 21 function metrics grouped into four points of comparison. Each point of comparison is used to compare functions and determine their cloning level. An ordinal scale of eight cloning levels is defined. The levels range from an exact copy to distinct functions. The metrics, the thresholds and the process used are fully described. The results of applying the clone detection technique to two telecommunication monitoring systems totaling one million lines of source code are provided as examples. The information provided by this study is useful in monitoring the maintainability of large software systems.

Enhanced Measurement for Early Risk Assessment of Latent Defects (EMERALD) is a decision support system for assessing reliability risk. It is used by software developers and managers to improve telecommunications software service quality as perceived by the customer and the end user. Risk models are based on static characteristics of source code. This paper shows how a system such as EMERALD can enhance software development, testing, and maintenance by integration of: a software quality improvement strategy; measurements and models; and delivery of results to the desktop of developers in a timely manner. This paper also summarizes empirical experiments with EMERALD's models using data from large industrial telecommunications software systems. EMERALD has been applied to a very large system with over 12 million lines of source code within procedures. Experience and lessons learned are also discussed.

Telecommunications software is known for its high reliability. Society has become so accustomed to reliable telecommunications, that failures can cause major disruptions. This is an experience report on application of discriminant analysis based on 20 static software product metrics, to identify fault prone modules in a large telecommunications system, so that reliability may be improved. We analyzed a sample of 2000 modules representing about 1.3 million lines of code, drawn from a much larger system. Sample modules were randomly divided into a fit data set and a test data set. We simulated utilization of the fitted model with the test data set. We found that identifying new modules and changed modules mere significant components of the discriminant model, and improved its performance. The results demonstrate that data on module reuse is a valuable input to quality models and that discriminant analysis can be a useful tool in early identification of fault prone software modules in large telecommunications systems. Model results could be used to identify those modules that would probably benefit from extra attention, and thus, reduce the risk of unexpected problems with those modules.