Efficient Preparation and Utilization of Test Data

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With computers being the heart of today�s world, applications being built need to be properly tested. Good quality test data is one of the major factors contributing to successful testing. Efficient test data management is imperative in ensuring software quality. The fact that test data plays a vital role not only in testing but also the entire software lifecycle process is often forgotten. By creating quality test data, defects can be detected at an early stage in the software lifecycle process which in turn helps to reduce cost and time to market and improves quality.

But is it easy to create quality test data? How to manage it effectively? Can the effort for arranging test data be reduced? These are some of the questions that arise when project teams strategize about test data. While technology is enabling faster and richer data retention, the real challenge still lies in preparing quality test data and making good use of it.

The intent of this paper is to discuss an approach for the creation and utilization of test data thereby improving the quality and coverage of testing software applications.

Introduction

In today�s world, all the organizations have three critical business goals: Improving Business Agility, Increasing Revenue and Mitigating Possible Risks to the business. The realization of these goals is the basis of success for any organization.

To help the organizations achieve these goals, IT and Business teams need to deliver quality products which are accepted by the customers. The IT team needs to deliver quality software on time and within the allocated budget. The basic building block of a coherent strategy for Software Quality is proper testing which in turn is based on appropriate and accurate test data. Most teams find that the hardest part of testing is finding the test data which fits the pre requisites for testing. Well-planned data provides flexibility and helps reduce the cost of testing and further maintenance quite a lot. Generating the entire test data manually is not feasible as it is too slow and error-prone, and it can never prove the reliability of an application with the same level of confidence as real data. So, how can you acquire good quality test data, and how can it be managed effectively? This is where test data management becomes an important part of your overall testing strategy.

Some of the problems that arise with not having an effective test data management strategy are Inadequate testing, Increased time-to-market, Increased costs from redundant operations and rework and Non-compliance with regulatory norms on data confidentiality. Robust test data management processes are essential in maintaining applications and databases. In addition to this, the recent rise in identity theft, industry regulators and law makers continue to put pressure on organizations that prefer to use non standard techniques to provision test data.

Problem Statement

Test Data management and preparation of test data seems very simple but there are quite a few challenges involved:
� Realistic data is difficult to collect - With today�s huge business applications; data is typically spread across multiple systems and databases. This makes data extraction a time-consuming process and also the testers have limited skills for dealing with the range of databases and schemas. It all adds up to a lot of lost time during the testing process.
� Complexity of requirements - Requirements are quite complex and thus preparation of test data for fulfilling all given requirements becomes very complex and may require understanding of various domains and systems.
� High Storage costs - As the number of business applications rises and the amount of data they handle explodes, storage maintenance costs are becoming a significant drain on IT budgets. Given the high cost of storage maintenance, your QA team needs to reduce the amount of data it stores and manages. It is not cost effective to clone and maintain an entire production database when you actually need just a relevant subset of the data for testing.
� Using Non-Referentially Intact data - It is hard to maintain the referential integrity of data when the data is taken out of a production environment or created manually. � Sudden Application Changes - Sometimes the application may undergo a change suddenly and immediate requests for test data have to be catered by the testing team incorporating the changes that have occurred.
� Data Confidentiality - Social security numbers, credit card numbers and other personal and business information are an attractive target to hackers, data thieves and others. When production data is used for QA tests, we need to ensure that information is available only to authorized users.
� Test data exhaustion - During testing cycles, there are chances that test data gets used and cannot be reused again.

Proposed Solution

It is critical to generate accurate test data so as to test how an application would behave in production. Good quality test data is the key to testing existing and new applications. The traditional approach of extracting data from production and loading it to the test environment is not desirable as the costs involved with this activity is high. Manually creating test data would require a lot of effort. What we require is an approach which would reduce the test data management efforts ensuring a smooth and well defined workflow.