Your Research Problem

By September the 14th, submit a clear, precise statement of a computer science problem for research.

Be sure

problem is stated in a complete, grammatical sentence
is clear how the area of study will be limited or focused
is more than a simple exercise in gathering information, answering a yes/no question or making a simple comparison
a result (solution) can be given in a year's time frame

Here are some examples of the problem statements that I presented in lecture from the SERC researchers. Please remember that these were part of a slide presentation and the description may be briefer than in the original proposal. However, reading them you can get the flavor of problem, approach (method) and goal.

Data Quality
Ahmed Elmagarmid
Purdue University

Problem: Approaching data quality in a more comprehensive way than what is currently being done. Existing scrubbing tools fail to realize that data is a complex entity that is replicated, dynamic, and tightly coupled to the business processes

Approach: (a) Define a framework for data quality: Including dimensions, requirements, policies, and a taxonomy for dealing with and improving data quality, and, (b) Develop a methodology that deals properly with the issues of initial cleanup, continuous cleanup, and process improvements. The methodology must be comprehensive, implementable, and measurable.

Goals: Data quality has been a major cause of customer dissatisfaction, lost business, and inability to meet business objectives. While quality has a clearer meaning when it comes to software, it is much more poorly defined when it comes to data. A generally acceptable and operational definition of data quality refers to Accuracy, Consistency, Currency, and Completeness of data. The specific goal of this project is to develop a methodology that deals with all aspects of data quality.

Design Metrics
Dolores and Wayne Zage
Ball State University

Description: During the software design process, developers should be able to infer more about the software they are developing. Computing metrics allows one to choose the best design, as well as identify stress points that may lead to difficulty during coding and maintenance. (Currently developing OO design metrics.)

Method:

Use metrics D_e and D_i and D(G) to identify error-prone modules in a software design.

Use DB (design balance) and DC (design connectivity) to gain a better understanding of a developing system.

Various strategies have been employed by current affiliates to correct or monitor troublesome modules.

Goal:

Improve software development processes and products.

Predicting the Performance of Software Process Improvements
David Raffo, Portland State University

Description: Previous research: Process Tradeoff Method (PTA) predicts the impact of process improvements on:

development cost

product quality

task schedule

Current Project:

Further develop these methods by applying the techniques to address a specific question: What is the impact of implementing the Personal Software Process (PSP) in a real world development setting?

Goal:

Accommodate new processes, process changes, and project environments

Develop graphical and quantitative process models which can generally be used to predict the impact of implementing CMM KPAs and other process changes

Analysis of Software Process Models and Metrics:
Moving to Levels 4 and 5 of the CMM
David Raffo & Warren Harrison
Portland State University

Description: Develop innovative application of TQM principles to software project management using software process models and metrics.

Approach: Develop a suite of software process and product metrics along with software process models which together can be used in an integrated framework to support project management and process planning decisions.

Goal: Develop a systematic approach to help companies achieve the Quantitative Process Management, Software Quality Management, and the Continuous Process Improvement KPAs of the CMM.

Estimating Software Reliability During Conceptual Design
Bruce D’Ambrosio
Oregon State University

Description: Traditional reliability estimation methods depend on data from product testing. However, many software technologies are evolving so rapidly that failure rates of existing systems may not provide a good indication of reliability of systems under design and development, even in the early, conceptual design phase.

Approach:

Develop models of software reliability based on expert judgement combined with statistical information. Exploit advances in Bayesian probability theory to develop software reliability models, which can combine expert estimates of factors influencing reliability with statistical information.

Goal:

Develop methods and tools for predicting the reliability of complex software systems during early design.

Computer-aided Testing for Reusable Software Components
Mark Yang
University of Florida

Description: Reuse has been widely discussed and promoted in the software industry, but many aspects of current practice has to be modified in order for reuse to be practical. For example, the change of reliability when a component is used in a new environment has not been well investigated. Without some guidelines, we may not have the confidence to reuse a component. How should the component be re-tested, and if necessary be modified

Approach:

To study reliability estimation methods under a changing environment

To figure out the number of new test cases needed in order to meet the reliability requirement in a new environment

To build a tool that takes the old testing results, usage records, new environments, and new reliability requirement as inputs and outputs the number of new test cases

To assess software reliability estimation methods from the reuse point of view. Which one is the best choice for reuse?

Goal: To build a tool that can certify reusable components based on past testing history and present requirements

Testing Distributed Systems
Aditya Mathur and Sudipto Ghosh
Purdue University

Description:

Testing distributed systems composed of software components for tolerance to component failures, security and correctness of implementation.

Developing interface based fault injection methods between components and metrics in order to assess the adequacy of testing.

Method:

Identify the source of errors and failures in a distributed system

Develop and validate metrics to assess test adequacy

Study the behavior of system under component failure

Goal:

Develop a methodology to assess the tolerance of a distributed system to failures occurring in one or more of its components
Develop and assess software fault injection testing methodology for distributed systems
Develop metrics for the assessment of adequacy of tests of distributed systems

Develop a prototype assessment tool for experimentation

Architecture Based Estimation of Software Reliability
Aditya Mathur, Pietro Michielan, Manuela Schiona
Purdue University

Description:

Traditional methods for software reliability assessment treat a software system as a "whole" and are applicable very late in the development cycle

We have proposed CBRE (Component-Based Reliability Estimation) to overcome the problems associated with traditional methods.

CBRE can also be applied to obtain early estimates of system reliability, i.e., prior to code availability.

The current focus is on

comparing CBRE with Laprie-Kanoun method of component-based reliability estimation

evolving models of inter- and intra-component dependencies,

determining ways of applying CBRE in early stages of development.