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

__________________________________________________________________________________________This research presents a systematic literature review of motivation in Software Engineering. The objective is to report on what motivates and de-motivates developers, and how existing models address motivation. The majority of studies find Software Engineers form a distinguishable occupational group. Results indicate that Software Engineers are likely to be motivated according to: their 'characteristics' (e.g., their need for variety); internal 'controls' (e.g., their personality) and external 'moderators' (e.g., their career stage). Models of motivation in Software Engineering are disparate and do not reflect the complex needs of Software Engineers in their different career stages, cultural and environmental settings.

Background Despite decades of effort focused on improvement of engineering education, many recent advances have not resulted in systemic change. Diffusion of innovations theory is used to better understand this phenomenon. Purpose (Hypothesis) Research questions include: How widespread is awareness and adoption of established engineering education innovations? Are there differences by discipline or institutional type? How do engineering department chairs find out about engineering education innovations? What factors do engineering department chairs cite as important in adoption decisions? Design/Method U.S. engineering department chairs were surveyed regarding their awareness and department use of seven engineering education innovations. One hundred ninety‐seven usable responses are presented primarily as categorical data with Chi square tests where relevant. Results Overall, the awareness rate was 82 percent, while the adoption rate was 47 percent. Eighty‐two percent of engineering departments employ student‐active pedagogies (the highest). Mechanical and civil engineering had the highest rates, in part due to many design‐related innovations in the survey. Few differences by institution type were evident. In the past, word of mouth and presentations were far more effective than publications in alerting department chairs to the innovations. Department chairs cited financial resources, faculty time and attitudes, and student satisfaction and learning as major considerations in adoption decisions. Conclusions The importance of disciplinary networks was evident during survey administration and in the results. Specific recommendations are offered to employ these networks and the engineering professional societies for future engineering education improvement efforts.