Veena Mendiratta of Alcatel-Lucent will give three lectures on September 30, 2013, as part of the SIAM Visiting Lecturer Program, sponsored by the Mathematics Department and the S-COAM program.
What Can We Learn from Software Failure Data
- 1:25–2:15 p.m.
- Room 101 Eberly
detection and fault correction are vital to ensure high-quality software.
During the development and deployment phases, detected failures are commonly
classified by severity and tracked to meet quality and reliability
requirements. Besides tracking failures, this data can be analyzed and used to
qualify the software and to control the development and maintenance process.
Our work is focused on failure data collected during the development phase and
explores what we can learn by analyzing this data. Change management systems
log the failures detected and the code fixes to correct the underlying software
defects. By applying software reliability models and statistical techniques to
this defect data, we can answer questions such as the following:
- Is the maintenance process increasing the
- Is the maintenance process under control?
- How many failures are expected to occur in the
- What is the expected time remaining to meet the reliability
presentation addresses these questions by using a methodology based on trend
analysis, control charts, and software reliability growth models. The
methodology is applied to a large software system during various stages of
testing including customer acceptance testing.
What is new about this methodology is the combined use of control
charts, trend analysis, and software reliability models.
Career Talk for Undergraduates
- 3:35–4:35 p.m.
- Rooms 226/229 Stright
Using Social Influence to Predict Subscriber Churn
- 5:20–6:20 p.m.
- Rooms 226/229 Stright
saturation of mobile phone markets has resulted in rising costs for operators
to obtain new customers. These operators thus focus their energies on
identifying users that will churn so they can be targeted for retention
campaigns. Typical churn prediction algorithms identify churners based on
service usage metrics, network performance indicators, and demographic information.
Social and peer-influence to churn, however, is usually not considered. In this
talk, a new churn prediction algorithm is described that incorporates the influence
churners spread to their social peers. Using data from a major service provider,
it is shown that social influence improves churn prediction and is among the
most important factors.
Veena Mendiratta is a practice leader, Network
Reliability and Analytics, in the Corporate CTO organization at Alcatel-Lucent
in Naperville, Illinois, USA. Her work is focused on analytics for
customer experience and network reliability, cloud network reliability, and
service reliability modeling for mission critical networks. She has published
over 40 papers in conferences and journals and has presented tutorials on
reliability modeling and analysis at several conferences. Other
professional activities include: Scientific Committee member for the NetMob
Conference, Program Committee member for IEEE, DSN, and ISSRE conferences (past)
and IEEE Cloud Engineering conference; Steering Committee member for the ISSRE
conference; member of the SIAM Visiting Lecturer Program; invited judge
for the annual COMAP-sponsored MCM and HiMCM math modeling competitions; and
appointment as a Fulbright Specialist Scholar for a five-year period
(2012–2017). She holds a Ph.D. in
Operations Research from Northwestern University and a B.Tech in Engineering
from the Indian Institute of Technology, New Delhi, India.
Department of Mathematics