@article {31, title = {Estimation of Defects Based on Defect Decay Model: ED^\{3\}M}, journal = {IEEE Transactions on Software Engineering}, volume = {34}, year = {2008}, pages = {336 - 356}, abstract = {

An accurate prediction of the number of defects in a software product during system testing contributes not only to the management of the system testing process but also to the estimation of the product{\textquoteright}s required maintenance. Here, a new approach called ED3M is presented that computes an estimate of the total number of defects in an ongoing testing process. ED3M is based on estimation theory. Unlike many existing approaches the technique presented here does not depend on historical data from previous projects or any assumptions about the requirements and/or testers{\textquoteright} productivity. It is a completely automated approach that relies only on the data collected during an ongoing testing process. This is a key advantage of the ED3M approach, as it makes it widely applicable in different testing environments. Here, the ED3M approach has been evaluated using five data sets from large industrial projects and two data sets from the literature. In addition, a performance analysis has been conducted using simulated data sets to explore its behavior using different models for the input data. The results are very promising; they indicate the ED3M approach provides accurate estimates with as fast or better convergence time in comparison to well-known alternative techniques, while only using defect data as the input.

}, keywords = {Costs, defect decay model, defect estimation, Defect prediction, estimation theory, Inspection, Metrics/Measurement, Phase estimation, Productivity, program testing, Programming, Software maintenance, software metrics, software product, Software systems, Software testing, Statistical methods, System testing, system testing process, Testing and Debugging}, doi = {10.1109/TSE.2008.23}, author = {Haider, Syed and Cangussu, Jo{\~a}o and Cooper, Kendra and Ram Dantu} } @conference {60, title = {Quantifying presence using calling patterns}, booktitle = {Communication Systems Software and Middleware and Workshops, 2008. COMSWARE 2008. 3rd International Conference on}, year = {2008}, month = {Jan}, abstract = {

Presence technology is going to be an integral part of the next generation of communication technology. It can eliminate telephone tag between two parties (caller and callee), which will increase productivity of the parties and reduce unnecessary bandwidth usage of unwanted calls. In this paper, we propose a Willingness Estimator that computes willingness level of a callee for receiving calls from a specified caller. By knowing the willingness value of the callee the caller can decide on proceeding with the call or not. The proposed Willingness Estimator is tested with real mobile user data, and these results are highly accurate. We measure willingness based on calling patterns (arrival time, location, day) of a caller to a callee and this could serve as one of the future presence based services. The results can be used in telecommunication networks such as PSTN, Cellular networks, and Voice over IP networks.

}, keywords = {cellular networks, Communications technology, Computer science, Computer security, Intrusion detection, Laboratories, Mobile handsets, mobile user data, Network servers, next generation communication, Next generation networking, presence technology, Productivity, PSTN, telecommunication networks, telephony, user calling pattern, voice over IP networks, willingness estimator}, doi = {10.1109/COMSWA.2008.4554403}, author = {Husna, Husain and Phithakkitnukoon, Santi and Baatarjav, Enkh-Amgalan and Ram Dantu} } @conference {56, title = {Quantifying the Presence of Phone Users}, booktitle = {2008 5th IEEE Consumer Communications and Networking Conference}, year = {2008}, month = {Jan}, abstract = {

Presence-enabled telephony services can reduce telephone tag and improve customer satisfaction. In this paper we proposed the Bayesian inference model to calculate the willingness level of the callee to accept calls. Before making a call, the caller may use the willingness calculator to find out whether the callee is available. Based on this level the user can make a decision whether to make a call. For validation of our results, we used actual call logs of 100 users collected at MIT by the Reality Mining Project group for a period of 8 months. We used time of the day, day of the week, talk-time and location for calculating the willingness level Our results show a good agreement between computed willingness level and the number of missed/rejected calls. This service can be included as part of the presence server. When deployed, this service can increase productivity, avoid unwanted calls and reduce the call traffic congestion. This service is beneficial to both subscribers and phone service providers. However, in order to make this service a reality, we need to take into account other factors such social closeness, proximity, multiplexity and reputation of the caller.

}, keywords = {Bayes methods, Bayesian inference model, Bayesian methods, call traffic congestion, Computer science, Context, customer satisfaction, Multimedia communication, Multimedia systems, Network servers, phone service provider, presence-enabled telephony service, Productivity, Roads, telecommunication congestion control, telecommunication traffic, telephony, willingness calculator}, doi = {10.1109/ccnc08.2007.206}, author = {Zhang, Huiqi and Ram Dantu} }