Monica Johar

A Comparison of Pair Versus Solo Programming Under Different Objectives: An Analytical Approach

This study compares the performances of pair development (an approach in which a pair of developers jointly work on the same piece of code), solo development, and mixed development under two separate objectives: effort minimization and time minimization. To this end, we develop analytical models to optimize module-developer assignments in each of these approaches. These models are shown to be strongly NP-hard and solved using a genetic algorithm. The solo and pair development approaches are compared for a variety of problem instances to highlight project characteristics that favor one of the two practices. We also propose a simple criterion that can reliably recommend the appropriate approach for a given problem instance. Typically, for efficient knowledge sharing between developers or for highly connected systems, the pair programming approach is preferable. Also, the pair approach is better at leveraging expertise by pairing experts with less skilled partners. Solo programming is usually desirable if the system is large or the effort needed either to form a pair or to code efficiently in pairs is high. Solo programming is also appropriate for projects with a tight deadline, whereas the reverse is true for projects with a lenient deadline. The mixed approach (i.e., an approach where both the solo and pair practices are used in the same project) is only indicated when the system consists of groups of modules that are sufficiently different from one another.

Content Provision Strategies in the Presence of Content Piracy

We consider a publisher that earns advertising revenue while providing content to serve a heterogeneous population of consumers. The consumers derive benefit from consuming content but suffer from delivery delays. A publishers content provision strategy comprises two decisions: (a) the content quality (affecting consumption benefit) and (b) the content distribution delay (affecting consumption cost). The focus here is on how a publisher should choose the content provision strategy in the presence of a content pirate such as a peer-to-peer (P2P) network. Our study sheds light on how a publisher could leverage a pirates presence to increase profits, even though the pirate essentially encroaches on the demand for the publishers content. We find that a publisher should sometimes decrease the delivery speed but increase quality in the presence of a pirate (a quality focused strategy). At other times, a distribution focused strategy is better; namely, increase delivery speed, but lower quality. In most cases, however, we show that the publisher should improve at least one dimension of content provision (quality or delay) in the presence of a pirate.

A Benchmarking Model for Management of Knowledge-Intensive Service Delivery Networks

Effective management of information technology (IT) and IT-enabled services is becoming increasingly important due to the growing complexity of their context. These services are often delivered by employees who work at widely dispersed locations and interact with each other to constitute knowledge-intensive service delivery networks (KISDNs). This paper contributes to the effective design and management of KISDNs by presenting a mixed-integer programming model that integrates disparate streams of research. This model facilitates analysis and managerial benchmarking of KISDN performance. It captures how the performance of such networks depends on the interaction between workflow decisions, structure of information flow networks (IFNs), and knowledge management decisions. We propose that knowledge about IFNs and worker competence can be effectively used to make workflow decisions. Our results, based on the study of different IFN archetypes, illustrate practices for effective management of KISDNs. Managers can enhance business value by recognizing existing IFNs, increasing randomness in IFNs, nurturing weak or performative ties depending on the archetype, assigning tasks based on effective worker competence, and selectively delaying assignment of tasks to workers. In addition, our results illustrate the impact of training and network density on KISDN performance.

Analyzing Sharing in Peer-to-Peer Networks Under Various Congestion Measures

Historically, the use of peer-to-peer (P2P) networks has been limited primarily to user-initiated exchanges of (mostly music) files over the Internet. This traditional view of P2P networks is changing, however, and the use of P2P networks has been suggested for delivering general-purpose content over the Web (or corporate intranets), even in real time. We analyze sharing in a P2P community in this new context under three different congestion measures: delay, jitter, and packet loss. Sharing is important to study in the presence of congestion because most existing research on P2P networks views congestion in the network as a relatively insignificant criterion. However, when delivering general-purpose content, congestion and its relationship to sharing is a critical factor that influences end-user performance. This paper looks at P2P networks from this new perspective by explicitly considering the effects of congestion on user incentives for sharing. We also propose a simple incentive mechanism that induces socially optimal sharing.

Selling vs. Profiling: Optimizing the Offer Set in Web-Based Personalization

We study the problem of optimally choosing the composition of the offer set for firms engaging in web-based personalization. A firm can offer items or links that are targeted for immediate sales based on what is already known about a customers profile. Alternatively, the firm can offer items directed at learning a customers preferences. This, in turn, can help the firm make improved recommendations for the remainder of the engagement period with the customer. An important decision problem faced by a profit maximizing firm is what proportion of the offer set should be targeted toward immediate sales and what proportion toward learning the customers profile. We study the problem as an optimal control model, and characterize the solution. Our findings can help firms decide how to vary the size and composition of the offer set during the course of a customers engagement period with the firm. The benefits of the proposed approach are illustrated for different patterns of engagement, including the length of the engagement period, uncertainty in the length of the period, and the frequency of the customers visits to the firm. We also study the scenario where the firm optimizes the size of the offer set during the planning horizon. One of the most important insights of this study is that frequent visits to the firms website are extremely important for an e-tailing firm even though the customer may not always buy products during these visits.

Optimal software design reuse policies: A control theoretic approach

We study optimal policies for design reuse in a software project using a control theoretic approach. A project needs to be developed in minimum time by optimally choosing the proportion of effort allocated towards design activities. We first consider a local case, where the benefits of design activities are restricted within the project. Here the optimal policy follows a bang-bang structure: invest fully in design followed by investing fully in implementation. Next we consider two sequentially linked projects where design capital built in the first project can be reused by the downstream project. For the two project case, the design investment in the upstream project first increases and then decreases with the ability to utilize design capital accumulated in the first project for the second project. We also propose a coordination scheme that rewards the upstream project team so that design investment in this project is chosen in a globally optimal manner.

Optimal Pricing and Workforce Composition for Service Delivery Using a Hybrid Workforce (Research in Progress)

Innovative work arrangements are increasingly being enabled by technology. On-demand technology-enabled marketplaces for a variety of skills are becoming popular. Examples include topcoder.com and odesk.com. This paper focuses on how organizations can use such on-demand marketplaces along with in-house workers to staff dynamic knowledge-intensive service environments. An economic analysis of prices paid to on-demand workers is presented. Such an analysis can be further developed to characterize the optimal workforce composition of the in-house workforce.