attributes of measures

attributes of measures

Productivity in a software development project is substantially different from productivity in manufacturing industries. In the manufacturing industry, productivity is the result of capital, technology, human resource, competence, and skill of management. In software development, capital and equipment play a very nominal role. In software development, no raw material or bought-out component is used. However, in manufacturing industry, these two components form a very significant part and these determine, to a great extent, the productivity. Given this kind of difference, a study for productivity in the manufacturing industry cannot be straightaway applied to the software industry. If one takes a precise view of the software industry, one observes that the key factor determining productivity in this industry is the human resource. Hence, in this chapter measuring the software output in relation to manpower deployed has been considered for measuring productivity. Further, while dozens of studies are available in manufacturing industries, there is hardly any study on productivity in software industry.

  In the early 1990s, IT industry, which includes both hardware and software, was considered as a sunrise industry. After the mid-1990s it became clear that this industry was going to occupy a prominent place and the impetus for this industry's growth was coming from revolutionary changes in telecommunications, supply chain management, utilities, insurance and banking sector, greater use of satellites, etc. With increasing competition and rising cost of skilled manpower, attention to productivity was becoming inevitable in such industry. While in the early 1990s hardware had the preeminence over software, in the late 1990s and early 2000 software has taken a pre-eminent place. In view of these changes, the need for efficiency has been increasingly felt in the software industry. This need has become essentially the driving force for the present study.

  High productivity implies that given the number of function points in a project, it has consumed fewer man-months. However, it is possible that with less man-month being spent, the project may be completed in a hurry and may result in high delivered defects, which can create customer dissatisfaction. It is therefore important that while less effort is spent, no relaxation should be made on the final quality of the deliverable. In other words, while a higher value of productivity is maintained, better quality software with less or no delivered defects should be guaranteed. Such guarantee can help in increasing efficiency and eventually result in higher profitability and higher return on investment. In this chapter, a detailed approach for measuring metrics has been provided.

 

Table 3.2 gives an overview of basic measures, their typical attributes, and data capture mechanism. The list of tools given in Table 3.2 is used for capturing data during the project execution. At the completion of a project, data related to all lifecycle stages are summarized and captured. For an ongoing maintenance project, usually these data are captured at the end of a defined period of time (e.g., data for maintenance project can be captured at the end of 3 months and then metrics is derived).