Prior work on software release policy implicitly assumes that testing stops at the time of software release. In this research, we propose an alternative release policy for custom-built enterprise-level software projects that allows testing to continue for an additional period after the software product is released. Our analytical results show that the software release policy with postrelease testing has several important advantages over the policy without postrelease testing. First, the total expected cost is lower. Second, even though the optimal time to release the software is shortened, the reliability of the software is improved throughout its lifecycle. Third, although the expected number of undetected bugs is higher at the time of release, the expected number of software failures in the field is reduced. We also analyze the impact of market uncertainty on the release policy and find that all our prior findings remain valid. Finally, we examine a comprehensive scenario where in addition to uncertain market opportunity cost, testing resources allocated to the focal project can change before the end of testing. Interestingly, the software should be released earlier when testing resources are to be reduced after release.
Many software products are available free of charge. While the benefits resulting from network externality have been examined in the related literature, the effect of free offer on the diffusion of new software has not been formally analyzed. We show in this study that even if other benefits do not exist, a software firm can still benefit from giving away fully functioning software. This is due to the accelerated diffusion process and subsequently the increased net present value of future sales. By adapting the Bass diffusion model to capture the impact of free software offer, we provide a methodology to determine the optimal number of free adopters. We show that the optimal free offer solution depends on the discount rate, the length of the demand window, and the ratio of low-valuation to high-valuation free adopters. Our methodology is shown to be applicable for both fixed and dynamic pricing strategies.
We consider a new variety of sequential information gathering problems that are applicable for Web-based applications in which data provided as input may be distorted by the system user, such as an applicant for a credit card. We propose two methods to compensate for input distortion. The first method, termed knowledge base modification, considers redesigning the knowledge base of an expert system to best account for distortion in the input provided by the user. The second method, termed input modification, modifies the input directly to account for distortion and uses the modified input in the existing (unmodified) knowledge base of the system. These methods are compared with an approach where input noise is ignored. Experimental results indicate that both types of modification substantially improve the accuracy of recommendations, with knowledge base modification outperforming input modification in most cases. Knowledge base modification is, however, more computationally intensive than input modification. Therefore, when computational resources are adequate, the knowledge base modification approach is preferred; when such resources are very limited, input modification may be the only viable alternative.