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.
While organisations recognise the advantages offered by global software development, there are many socio-technical barriers that affect successful collaboration in this inter-cultural environment. In this paper we present a review of the global software development literature where we highlight collaboration problems experienced by a cross-section of organisations in twenty-six studies. We also look at the literature to answer how organisations are over-coming these barriers in practice. We build on our previous study on global software development where we define collaboration as four practices related to agreeing, allocating, and planning goals, objectives, and tasks among distributed teams.We found that the key barriers to collaboration are geographic, temporal, cultural, and linguistic distance; the primary solutions to overcoming these barriers include site visits, synchronous communication technology, and knowledge sharing infrastructure to capture implicit knowledge and make it explicit.
Original article can be found at : http://www.sciencedirect.com/ Copyright Elsevier [Full text of this article is not available in the UHRA]Motivation in software engineering is recognized as a key success factor for software projects, but although there are many papers written about motivation in software engineering, the field lacks a comprehensive overview of the area. In particular, several models of motivation have been proposed, but they either rely heavily on one particular model (the job characteristics model), or are quite disparate and difficult to combine. Using the results from our previous systematic literature review (SLR), we constructed a new model of motivation in software engineering. We then compared this new model with existing models and refined it based on this comparison. This paper summarises the SLR results, presents the important existing models found in the literature and explains the development of our new model of motivation in software engineering
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