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We are grateful Financial support Institute, Carnegie I 1 3 4 1. An Empirical Analysis of Productivity and Quality in Software Products Abstract We examine the relationship between life-cycle productivity and conformance quality in software products. The effects of product size, personnel capability, software process, usage of tools and higher front-end investments on productivity and conformance quality were analyzed to derive managerial implications based on primary data collected on commercial software projects from a leading vendor.

Cap and trade program design options f9 key findings are as follows. First, our results provide evidence for significant increases in life-cycle productivity from improved conformance quality in software products shipped to the customers.

Given that the expenditure on computer software has been growing over the last few decades, empirical evidence for cost savings through quality improvement is a significant contribution to the cap and trade program design options f9. Second, our study identifies several quality drivers in software products. Our cap and trade program design options f9 indicate that higher personnel capability, deployment of resources in initial stages of product development especially design and improvements in software development process factors are associated with higher quality products.

It is now widely recognized that computer software accounts for a significant share of corporate information systems budgets [Humphrey, ]. Most corporate information systems depend on computer software for accurate and timely information. Software is also viewed as an important corporate asset in the s. Yet, the software community has long been faced with severe difficulties in delivering and supporting quality software products, on time [Blackburn, Scudder and van Wassenhove, a].

Additionally, life-cycle costs of software projects often turn out to be enormous and significantly over budget. In order to overcome these difficulties, quality management and related principles e.

Despite such efforts, the software industry continues to be plagued by an inability to develop quality software products [DeMarco, ]. In many organizations, productivity and schedules for software projects are largely unpredictable, and product quality is often poor. Software vendors are therefore attempting to build quality into the product by avoiding defects in the first place instead of removing the defects in the product through rigorous testing. However, the effect of these new software development processes on productivity or quality has not been empirically tested.

Thus, from a research perspective, it is important to identify the drivers of productivity and quality, and establish the relationship between productivity and quality. Our field study is based on primary data collected on commercial software projects of a leading vendor. We analyze the drivers of quality and productivity such as personnel capability, product size, software process factors and usage of tools.

We test the efficacy of improved processes and up-front investment in quality on life-cycle productivity of the projects. Our findings indicate significant increases in life-cycle productivity with improvements in quality. Managerial implications of our results are twofold. First, our results enable software product managers to assess cost savings from reducing the defects in their products shipped.

Second, our results provide managers with guidelines for resource deployment during product development. For instance, higher investments in the frontend of the product development cycle leads to higher quality. The rest of the paper is organized as follows.

In the next section, we briefly review the literature and highlight the contributions of our field study. We address the research issues in Section 3 and provide a theoretical basis for our empirical models in Section 4. We describe the data 3. Our empirical analysis is described in Section 6, the results in Section 7. Finally, we present conclusions and limitations of our study in Section 8. Literature Review and Contributions Identifying software productivity factors and estimating software costs continue to be important research topics [Kemerer, ; Mukhopadhyay and Kekre, ; Banker et al.

Recently, Maxwell, et al. Most of the empirical research on software maintenance has analyzed tradeoffs between software quality and maintenance effort, and identified drivers of software maintenance costs. Theoretical models have also been proposed to predict the quality or reliability level of software products [Farr, ]. However, practitioners in the software industry continue to face problems related to cost overruns. Prior research has not been able to cap and trade program design options f9 answers since productivity and quality modeling efforts have often considered either only the productivity or the quality.

That is, most productivity models ignore the quality of the delivered product, and quality models ignore the cost incurred in developing or maintaining the products. A key reason is that the accounting systems of software organizations cap and trade program design options f9 provisions for tracking life-cycle costs. Moreover, the effect of development process aspects has not been explicitly incorporated in cost or quality cap and trade program design options f9.

Empirical evidence on the effect of process factors is restricted to case studies and experience reports of a few projects.

Our field study fills this void. The three main contributions of our field study are: Most previous models address either development or maintenance costs separately. Theoretical Framework As noted earlier, software managers and executives face a multitude of choices that impact productivity and quality.

The major choices relate to technology, people, process, and product factors. However, the specific impacts of these factors on life-cycle productivity and quality are still not clear.

For instance, should a manager invest in the latest software development cap and trade program design options f9 and language, or in an automated tool to support software design? Likewise, should the manager hire new programmers or invest in process improvements? At present, the lack of a rigorous framework can lead to incorrect trade-off.

In our framework, we address the following research questions: What is the trade-off between quality and life-cycle productivity? What are the effects of the development process on life-cycle productivity and quality? Does up-front resource deployment pay off? What are the effects of development resources on productivity and quality?

We examine each of these questions in the subsections 3. There are many parallels between costs of quality in manufacturing and those in software development. The relationship between productivity and quality in the manufacturing context has been discussed from two viewpoints [Garvin, ]. The first perspective, a traditional viewpoint, asserts that increased expenditures are required to attain higher quality levels and highlights economic conformance levels of quality.

The second perspective considers the life-cycle cost of the product, 5. The claim made is that costs are inversely related to the quality attained and it is always optimal to produce products with zero defects [Crosby, ; Gyma, ].

The rationale for this view is that cost reduction and quality improvement can be simultaneously attained by reducing waste and rework, cap and trade program design options f9 that reducing defects in the product leads to substantial savings in support costs. We translate these perspectives to the software domain in our modeling effort. Disciplined methods and practices such as requirements analysis, cap and trade program design options f9 prevention, and configuration management are expected to result in better control over the software development process.

It has been observed in the literature that allocating more resources in the early stages may improve the quality and productivity of the software considerably [Blackburn, Scudder, and van Wassenhove, ; Humphrey, ]. The rationale behind this argument stems from the importance of requirements analysis lyand design. For instance, when customer requirements are not well mapped into the product design, customers may find more defects in the final product due to induced changes in the later stages of development.

As a result, the quality of the end product is likely to suffer. We examine the impact of varying deployment of resources in the early stages of product development on software conformance quality. All the software products at our research site were developed using the sequential life-cycle model often known as the "waterfall" model.

This systems development model specifies distinct stages of product development such as feasibility study, requirements analysis, detailed design, coding and unit testing, systems integration, and field maintenance [Boehm, ]. Researchers have used various measures such as average language experience, software domain experience and analyst experience as proxies for the technical capability of the teams.

Field studies in software projects have reported both positive and negative effects of tool deployment on software productivity [Banker et aL, ]. In order to control for these variations in productivity and quality, we include measures for both personnel capability and usage of tools in our model. Note that the effect of these drivers can be quantified after the software size has been accounted for.

Research Model and Data Collection In this section, we first develop the conceptual elements of our research model. We then cap and trade program design options f9 the research cap and trade program design options f9, and data collection methods to test the model. Cap and trade program design options f9 section ends with a description of the measures used for the variables in our model.

The model addresses the research questions related to tradeoffs between life-cycle productivity and quality and the effects of resource deployment and process design. The primary links of interest in our 7. The oval shapes and lighter arrows in Figure 1 show the control variables discussed in Section 3.

The life-cycle cost includes both development and support costs. Development costs include the costs incurred in all the stages before shipping the product to the field, whereas support costs are incurred in fixing customer reported problems.

Once the software product is released to the customers in the field, if the quality of the product is inadequate, customers report a significant number cap and trade program design options f9 problems. Hence, for a poor quality product, software vendors may incur substantial support costs to fix the problems cap and trade program design options f9 by the customers.

Thus we summarize the interaction between quality and life-cycle productivity depicted in Figure 1 using the following pair of equations. Research Site and Data Collection 8. Our research site is one of the largest software development laboratories of a Fortune company. This laboratory develops commercial software systems for various applications.

The annual revenue of the firm exceeds several hundred million dollars and the lab employs over software professionals. In the recent past, various practices for improving quality and productivity have been instituted. Considerable resources are being deployed at the design and planning stages of product development. In addition, efforts have been made to improve the process adopted for developing the cap and trade program design options f9 products.

Our study was initiated to assess the effectiveness of these programs. Our data collection involved gathering information on the costs and quality of a cross section of systems software products developed for various markets. We chose recent projects in order to control for the change in productivity or quality of the software projects due to tools and development technology.

Based on our discussions with the managers, we started with an initial sample of 56 projects.

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