Background: The accurate prediction of where faults are likely to occur in code can help direct test effort, reduce costs, and improve the quality of software. Objective: We investigate how the context of models, the independent variables used, and the modeling techniques applied influence the performance of fault prediction models. Method: We used a systematic literature review to identify 208 fault prediction studies published from January 2000 to December 2010. We synthesize the quantitative and qualitative results of 36 studies which report sufficient contextual and methodological information according to the criteria we develop and apply. Results: The models that perform well tend to be based on simple modeling techniques such as Naive Bayes or Logistic Regression. Combinations of independent variables have been used by models that perform well. Feature selection has been applied to these combinations when models are performing particularly well. Conclusion: The methodology used to build models seems to be influential to predictive performance. Although there are a set of fault prediction studies in which confidence is possible, more studies are needed that use a reliable methodology and which report their context, methodology, and performance comprehensivel

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This paper describes the results of an investigation into a set of metrics for object-oriented design, called the MOOD metrics. The merits of each of the six MOOD metrics is discussed from a measurement theory viewpoint, taking into account the recognized object-oriented features which they were intended to measure: encapsulation, inheritance, coupling, and polymorphism. Empirical data, collected from three different application domains, is then analyzed using the MOOD metrics, to support this theoretical validation. Results show that (with appropriate changes to remove existing problematic discontinuities) the metrics could be used to provide an overall assessment of a software system, which may be helpful to managers of software development projects. However, further empirical studies are needed before these results can be generalized.

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The concept of cohesion in a class has been the subject of various recent empirical studies and has been measured using many different metrics. In the structured programming paradigm, the software engineering community has adopted an informal yet meaningful and understandable definition of cohesion based on the work of Yourdon and Constantine. The object-oriented (OO) paradigm has formalised various cohesion measures, but the argument over the most meaningful of those metrics continues to be debated. Yet achieving highly cohesive software is fundamental to its comprehension and thus its maintainability. In this article we subject two object-oriented cohesion metrics, CAMC and NHD, to a rigorous mathematical analysis in order to better understand and interpret them. This analysis enables us to offer substantial arguments for preferring the NHD metric to CAMC as a measure of cohesion. Furthermore, we provide a complete understanding of the behaviour of these metrics, enabling us to attach a meaning to the values calculated by the CAMC and NHD metrics. In addition, we introduce a variant of the NHD metric and demonstrate that it has several advantages over CAMC and NHD. While it may be true that a generally accepted formal and informal definition of cohesion continues to elude the OO software engineering community, there seems considerable value in being able to compare, contrast, and interpret metrics which attempt to measure the same features of software.

Abstract.Adoption of Information Technology (IT) in organizations is influenced by a range of factors in the context of technology, organization, environment and individuals. Amongst others, the IT literature has identified several organizational factors that either facilitates or hinders innovation adoption in organizations. Studies examining the factors influencing IT adoption have produced inconsistent and contradictory outcomes. We performed a meta-analysis of ten organizational factors to determine the relative impact and the strength of these attributes on IT adoption. The study aggregated the findings of past research to evaluate the magnitude and the direction of the relationship between organizational factors and IT innovation adoption. Results showed organizational readiness to be the most significant attribute. We also found a moderately significant relationship between IT adoption and Information Systems (IS) department size. The study found weak significance with IS infrastructure, top management support, IT expertise, resources and organizational size. Formalization, centralization and product champion were found to be insignificant attributes for IT adoption. The study also examined stage of innovation, type of innovation, type of organization and size of organization as four moderator conditions that affect the relationship between organizational variables and IT adoption.

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