Social scientists rarely take full advantage of the information available in their statistical results. As a consequence, they miss opportunities to present quantities that are of greatest substantive interest for their research and express the appropriate degree of certainty about these quantities. In this article, we offer an approach, built on the technique of statistical simulation, to extract the currently overlooked information from any statistical method and to interpret and present it in a reader-friendly manner. Using this technique requires some expertise,

The first goal of this paper is to empirically explore the relationships between existing object-oriented coupling, cohesion, and inheritance measures and the probability of fault detection in system classes during testing. In other words, we wish to better understand the relationship between existing design measurement in OO systems and the quality of the software developed. The second goal is to propose an investigation and analysis strategy to make these kind of studies more repeatable and comparable, a problem which is pervasive in the literature on quality measurement. Results show that many of the measures capture similar dimensions in the data set, thus reflecting the fact that many of them are based on similar principles and hypotheses. However, it is shown that by using a subset of measures, accurate models can be built to predict which classes contain most of the existing faults. When predicting fault-prone classes, the best model shows a percentage of correct clas...

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This paper aims at empirically exploring the relationships between most of the existing design coupling, cohesion, and inheritance measures for object-oriented (OO) systems, and the fault-proneness of OO system classes. The underlying goal of this study is to better understand the relationship between existing design measurement in OO systems and the quality of the software developed. In addition, we aim at assessing whether such relationships, once modeled, can be used to effectively drive and focus inspections or testing. The study described here is a replication of an analogous study conducted in a university environment with systems developed by students. In order to draw more general conclusions and to (dis)confirm the results obtained there, we now replicated the study using data collected on an industrial system developed by professionals. Results show that many of our findings are consistent across systems, despite the very disparate nature of the systems under study. Some of the strong dimensions captured by the measures in each data set are visible in both the university and industrial case study. For example, the frequency of method invocations appears to be the main driving factor of fault-proneness in all systems. However, there are also differences across studies, which illustrate the fact that, although many principles and techniques can be reused, quality does not follow universal laws and quality models must be developed locally, wherever needed.

This paper has not undergone formal review or approval of the faculty of the ILR School. It is intended to make results of Center research available to others interested in preliminary form to encourage discussion and suggestions. Page 1Managing Customer Services WP 00-07 This study examines the relationship between human resource practices, employee quit rates, and organizational performance by drawing on a unique nationally representative sample of 354 customer service and sales establishments in the telecommunications industry. Multivariate analyses show that quit rates are lower and sales growth is higher in establishments that emphasize high skills, employee participation in decision-making and in teams, and HR incentives such as high relative pay and employment security. Quit rates partially mediate the relationship between human resource practices and sales growth. These relationships also are moderated by the customer segment that frontline employees serve.

While the course of technological change is widely accepted to be highly uncertain and unpredictable, little work has identified or studied the ultimate sources and causes of that uncertainty. This paper proposes that purely technological uncertainty derives from inventors ’ search processes with unfamiliar components and component combinations. Experimentation with new components and new combinations leads to less useful inventions on average, but it also implies an increase in the variability that can result in both failure and breakthrough. Negative binomial count and dispersion models with patent citation data demonstrate that new combinations are indeed more variable. In contrast to predictions, however, the reuse of components has a nonmonotonic and eventually positive effect on variability.

In the context of software cost estimation, system size is widely taken as a main driver of system development effort. But, other structural design properties, such as coupling, cohesion, and complexity, have been suggested as additional cost factors. In this paper, using effort data from an object-oriented development project, we empirically investigate the relationship between class size and the development effort for a class and what additional impact structural properties such as class coupling have on effort. This paper proposes a practical, repeatable, and accurate analysis procedure to investigate relationships between structural properties and development effort. This is particularly important as it is necessary, as for any empirical study, to be able to replicate the analysis reported here. More specifically, we use Poisson regression and regression trees to build cost prediction models from size and design measures and use these models to predict system development effort. We also investigate a recently suggested technique to combine regression trees with regression analysis which aims at building more accurate models. Results indicate that fairly accurate predictions of class effort can be made based on simple measures of the class interface size alone (mean MREs below 30 percent). Effort predictions at the system level are even more accurate as, using Bootstrapping, the estimated 95 percent confidence interval for MREs is 3 to 23 percent. But, more sophisticated coupling and cohesion measures do not help to improve these predictions to a degree that would be practically significant. However, the use of hybrid models combining Poisson regression and CART regression trees clearly improves the accuracy of the models as compared to using Poisson regression alone.

Measuring structural design properties of a software system, such as coupling, cohesion, or complexity, is a promising approach towards early quality assessments. To use such measurement effectively, quality models are needed that quantitatively describe how these internal structural properties relate to relevant external system qualities such as reliability or maintainability. This chapter has for objective to summarize, in a structured and detailed fashion, the empirical results that have been reported so far with modeling external system quality based on structural design properties in object-oriented systems. We perform a critical review of existing work in order to identify lessons learned regarding the way these studies are performed and reported. Constructive guidelines are also provided to facilitate the work of future studies, thus facilitating the development of an

The tacit assumption that increased product modularity is associated with advantageous increases in organizational modularity underlies much of the literature on modularity. Previous empirical investigations of this assumption, few in number, have faced numerous confounding factors and generated conflicting results. I build a causal model for the relationship between product and organizational modularity, which I test using a distinctive empirical setting that controls for confounding factors present in previous studies. I find support for only part of the assumed relationship, showing that modularity is a more multi−faceted concept than previously recognized. In particular, increased product modularity enhances reconfigurability of organizations more quickly than it allows firms to move activities out of hierarchy. The paper contributes to the emerging stream of research that focuses on the previously under−appreciated costs of designing and maintaining a modular organization.