Background: The NASA datasets have previously been used extensively in studies of software defects. In 2013 Shepperd et al. presented an essential set of rules for removing erroneous data from the NASA datasets making this data more reliable to use.

Background: Ensemble techniques have gained attention in various scientific fields. Defect prediction researchers have investigated many state-of-the-art ensemble models and concluded that in many cases these outperform standard single classifier techniques. Almost all previous work using ensemble techniques in defect prediction rely on the majority voting scheme for combining prediction outputs, and on the implicit diversity among single classifiers. Aim: Investigate whether defect prediction can be improved using an explicit diversity technique with stacking ensemble, given the fact that different classifiers identify different sets of defects. Method: We used classifiers from four different families and the weighted accuracy diversity (WAD) technique to exploit diversity amongst classifiers. To combine individual predictions, we used the stacking ensemble technique. We used state-of-the-art knowledge in software defect prediction to build our ensemble models, and tested their prediction abilities against 8 publicly available data sets. Conclusion: The results show performance improvement using stacking ensembles compared to other defect prediction models. Diversity amongst classifiers used for building ensembles is essential to achieving these performance improvements.

Abstract Fowler et al. identified 22 Code Bad Smells to direct the effective refactoring of code. These are increasingly being taken up by software engineers. However, the empirical basis of using Code Bad Smells to direct refactoring and to address ‘trouble’ in code is not clear, i.e., we do not know whether using Code Bad Smells to target code improvement is effective. This paper aims to identify what is currently known about Code Bad Smells. We have performed a systematic literature review of 319 papers published since Fowler et al. identified Code Bad Smells (2000 to June 2009). We analysed in detail 39 of the most relevant papers. Our findings indicate that Duplicated Code receives most research attention, whereas some Code Bad Smells, e.g., Message Chains, receive little. This suggests that our knowledge of some Code Bad Smells remains insufficient. Our findings also show that very few studies report on the impact of using Code Bad Smells, with most studies instead focused on developing tools and methods to automatically detect Code Bad Smells. This indicates an important gap in the current knowledge of Code Bad Smells. Overall this review suggests that there is little evidence currently available to justify using Code Bad Smells. Copyright © 2010 John Wiley & Sons, Ltd.

Motivated software engineers make a critical contribution to delivering successful software systems. Understanding the motivations of software engineers and the impact of motivation on software engineering outcomes could significantly affect the industry's ability to deliver good quality software systems. Understanding the motivations of people generally in relation to their work is underpinned by eight classic motivation theories from the social sciences. We would expect these classic motivation theories to play an important role in developing a rigorous understanding of the specific motivations of software engineers. In this article we investigate how this theoretical basis has been exploited in previous studies of software engineering. We analyzed 92 studies of motivation in software engineering that were published in the literature between 1980 and 2006. Our main findings are that many studies of software engineers' motivations are not explicitly underpinned by reference to the classic motivation theories. Furthermore, the findings presented in these studies are often not explicitly interpreted in terms of those theories, despite the fact that in many cases there is a relationship between those findings and the theories. Our conclusion is that although there has been a great deal of previous work looking at motivation in software engineering, the lack of reference to classic theories of motivation means that the current body of work in the area is weakened and our understanding of motivation in software engineering is not as rigorous as it may at first appear. This weakness in the current state of knowledge highlights important areas for future researchers to contribute towards developing a rigorous and usable body of knowledge in motivating software engineers.

Current approaches to detecting bad smells in code are mainly based on software metrics. We suggest that these methods lack precision in detecting bad smells, and we propose a code pattern-based approach to detecting bad smells. However before such a pattern-based approach can be implemented, Fowler's original definitions of bad smells need to be made more precise. Currently Fowler's definitions are too informal to implement in a pattern-searching tool. In this paper we use an expert panel to evaluate our enhanced definitions for five of Fowler's bad smells. We use a questionnaire to survey four experts' opinions of our bad smell definitions. Our results show that the experts basically agree with our enhanced definitions of the message chains, middle man and speculative generality bad smells. However, there are strong disagreements on our definitions of the data clumps and switch statements bad smells. We present enhanced definitions on the basis of these expert opinions.

In this paper, we describe our investigation of the motivational differences between project managers and developers. Motivation has been found to be a central factor in successful software projects. However the motivation of software engineers is generally poorly understood and previous work done in the area is thought to be largely out-of-date. We present data collected from 6 software developers and 4 project managers at a workshop we organized at the XP2006 international conference.

Anecdotal evidence from our work with software developers suggests that maintenance is a significant problem for software development companies. A problem that is absorbing increasing amounts of precious development effort. In parallel, software companies are increasingly applying process improvement principles to development problems. In this paper we discuss how maintenance is addressed in process improvement programmes. We look at how well maintenance is addressed by formal process models like CMM. We also present empirical evidence from our study of process improvement in UK software companies. Our main findings are that although developers report that maintenance is indeed a problem, it is not always their most important problem. Furthermore, our findings also suggest that companies are often not well prepared for the maintenance phase of developments and that formal process improvement models do not pay enough attention to maintenance.