Model for Improving Productivity Without Impacting Quality of Deliverables in IT Projects

Literature Review

Boehm (1981), in an earlier study conducted with large government software projects as part of a sponsored government programme, found that cost of software projects is inversely proportional to variables that would help improve productivity. He found that high staff capability and low application complexity would improve productivity. However, no detail explanation was provided for measuring staff capability. Lawrence (1981) conducted a study in medium to large organization in Australia. He found that productivity increased with simpler application and better computing environment (CPU speed) being developed. These observations were similar to findings made by Mohanty (1981) and Kemerer (1987). Thadhani (1984), Conte (1986) and Lambert (1984) observed that good computing speed increases productivity.

In a study Boehm (1981), found that productivity of software development projects was influenced by different variables. The model used for cost estimation was called COCOMO which was used for estimating efforts in the software industry with success. The original COCOMO developed by Boehm was improved upon subsequently by many practitioners and researchers. Thus, COCOMO II had been developed as the improved version. The variables that are considered in COCOMO II model were broadly categorized into:

• 1.

  Personnel Variables: That included analyst capability, experience in developing applications in a particular domain, capability of a programmer, experience in technology, quality of training provided to increase skill level, attrition rate, and experience in working in a domain.

• 2.

  Product Variables: Similarly included required reliability of software, database size, complexity of software being developed, availability of reusable modules, and using a systematic process (such as ISO, CMM) so that effective documentation is prepared throughout the life cycle of the software.

• 3.

  Platform Variables: Comprised of database size, availability of computers, speed of computation and finally

• 4.

  Project Variables: Involved usage of software testing tools, modern programming practices (using software engineering standards), and requirements volatility.