Software effort estimation models are typically developed based on an underlying assumption that all data points are equally relevant to the prediction of effort for future projects. The dynamic nature of several aspects of the software engineering p
rocess could mean that this assumption does not hold in at least some cases. This study employs three kernel estimator functions to test the stationarity assumption in five software engineering datasets that have been used in the construction of software effort estimation models. The kernel estimators are used in the generation of nonuniform weights which are subsequently employed in weighted linear regression modeling. In each model, older projects are assigned smaller weights while the more recently completed projects are assigned larger weights, to reflect their potentially greater relevance to present or future projects that need to be estimated. Prediction errors are compared to those obtained from uniform models. Our results indicate that, for the datasets that exhibit underlying nonstationary processes, uniform models are more accurate than the nonuniform models; that is, models based on kernel estimator functions are worse than the models where no weighting was applied. In contrast, the accuracies of uniform and nonuniform models for datasets that exhibited stationary processes were essentially equivalent. Our analysis indicates that as the heterogeneity of a dataset increases, the effect of stationarity is overridden. The results of our study also confirm prior findings that the accuracy of effort estimation models is independent of the type of kernel estimator function used in model development.
Context: Interest in software engineering (SE) methodologies and tools has been complemented in recent years by research efforts oriented towards understanding the human processes involved in software development. This shift has been imperative given
reports of inadequately performing teams and the consequent growing emphasis on individuals and team relations in contemporary SE methods. Objective: While software repositories have frequently been studied with a view to explaining such human processes, research has tended to use primarily quantitative analysis approaches. There is concern, however, that such approaches can provide only a partial picture of the software process. Given the way human behavior is nuanced within psychological and social contexts, it has been asserted that a full understanding may only be achieved through deeper contextual enquiries. Method: We have followed such an approach and have applied data mining, SNA, psycholinguistic analysis and directed content analysis (CA) to study the way core developers at IBM Rational Jazz contribute their social and intellectual capital, and have compared the attitudes, interactions and activities of these members to those of their less active counterparts. Results: Among our results, we uncovered that Jazzs core developers worked across multiple roles, and were crucial to their teams organizational, intra-personal and inter-personal processes. Additionally, although these individuals were highly task- and achievement-focused, they were also largely responsible for maintaining positive team atmosphere, and for providing context awareness in support of their colleagues. Conclusion: Our results suggest that high-performing distributed agile teams rely on both individual and collective efforts, as well as organizational environments that promote informal and organic work structures.(Abridged)
Agile software developers are required to self-organize, occupying various informal roles as needed in order to successfully deliver software features. However, previous research has reported conflicting evidence about the way teams actually undertak
e this activity. The ability to self-organize is particularly necessary for software development in globally distributed environments, where distance has been shown to exacerbate human-centric issues. Understanding the way successful teams self-organise should inform distributed team composition strategies and software project governance. We have used psycholinguistics to study the way IBM Rational Jazz practitioners enacted various roles, expressed attitudes and shared competencies to successfully self-organize in their global projects. Among our findings, we uncovered that practitioners enacted various roles depending on their teams cohort of features; and that team leaders were most critical to IBM Jazz teams self-organisation. We discuss these findings and highlight their implications for software project governance.
Studying the human factors that impact on software development, and assigning individuals with specific competencies and qualities to particular software roles, have been shown to aid software project performance. For instance, prior evidence suggest
s that extroverted software project leaders are most successful. Role assignment based on individuals competencies and behaviors may be especially relevant in distributed software development contexts where teams are often affected by distance, cultural, and personality issues. Project leaders in these environments need to possess high levels of inter-personal, intra-personal and organizational competencies if they are to appropriately manage such issues and maintain positive project performance. With a view to understanding and explaining the specific competencies and behaviors that are required of project leaders in these settings, we used psycholinguistic and directed content analysis to study the way six successful IBM Rational Jazz leaders operated while coordinating their three distributed projects. Contrary to previous evidence reported in personality studies, our results did not reveal universal competencies and behaviors among these Jazz leaders. Instead, Jazz project leaders competencies and behaviors varied with their project portfolio of tasks. Our findings suggest that a pragmatic approach that considers the nature of the software tasks being developed is likely to be a more effective strategy for assigning leaders to distributed software teams, as against a strategy that promotes a specific personality type. We discuss these findings and outline implications for distributed software project governance.
The ability to self-organise is posited to be a fundamental requirement for successful agile teams. In particular, self-organising teams are said to be crucial in agile globally distributed software development (AGSD) settings, where distance exacerb
ates team issues. We used contextual analysis to study the specific interaction behaviours and enacted roles of practitioners working in multiple AGSD teams. Our results show that the teams studied were extremely task focussed, and those who occupied team lead or programmer roles were central to their teams self-organisation. These findings have implications for AGSD teams, and particularly for instances when programmers - or those occupying similar non-leadership positions - may not be willing to accept such responsibilities. We discuss the implications of our findings for information system development (ISD) practice.
Reliable empirical models such as those used in software effort estimation or defect prediction are inherently dependent on the data from which they are built. As demands for process and product improvement continue to grow, the quality of the data u
sed in measurement and prediction systems warrants increasingly close scrutiny. In this paper we propose a taxonomy of data quality challenges in empirical software engineering, based on an extensive review of prior research. We consider current assessment techniques for each quality issue and proposed mechanisms to address these issues, where available. Our taxonomy classifies data quality issues into three broad areas: first, characteristics of data that mean they are not fit for modeling; second, data set characteristics that lead to concerns about the suitability of applying a given model to another data set; and third, factors that prevent or limit data accessibility and trust. We identify this latter area as of particular need in terms of further research.
Context: Given the acknowledged need to understand the people processes enacted during software development, software repositories and mailing lists have become a focus for many studies. However, researchers have tended to use mostly mathematical and
frequency-based techniques to examine the software artifacts contained within them. Objective: There is growing recognition that these approaches uncover only a partial picture of what happens during software projects, and deeper contextual approaches may provide further understanding of the intricate nature of software teams dynamics. We demonstrate the relevance and utility of such approaches in this study. Method: We use psycholinguistics and directed content analysis (CA) to study the way project tasks drive teams attitudes and knowledge sharing. We compare the outcomes of these two approaches and offer methodological advice for researchers using similar forms of repository data. Results: Our analysis reveals significant differences in the way teams work given their portfolio of tasks and the distribution of roles. Conclusion: We overcome the limitations associated with employing purely quantitative approaches, while avoiding the time-intensive and potentially invasive nature of field work required in full case studies.
Small to medium sized business enterprises (SMEs) generally thrive because they have successfully done something unique within a niche market. For this reason, SMEs may seek to protect their competitive advantage by avoiding any standardization encou
raged by the use of packaged software (PS). Packaged software implementation at SMEs therefore presents challenges relating to how best to respond to misfits between the functionality offered by the packaged software and each SMEs business needs. An important question relates to which processes small software enterprises - or Small to Medium-Sized Software Development Companies (SMSSDCs) - apply in order to identify and then deal with these misfits. To explore the processes of packaged software (PS) implementation, an ethnographic study was conducted to gain in-depth insights into the roles played by analysts in two SMSSDCs. The purpose of the study was to understand PS implementation in terms of requirements engineering (or PSIRE). Data collected during the ethnographic study were analyzed using an inductive approach. Based on our analysis of the cases we constructed a theoretical model explaining the requirements engineering process for PS implementation, and named it the PSIRE Parallel Star Model. The Parallel Star Model shows that during PSIRE, more than one RE process can be carried out at the same time. The Parallel Star Model has few constraints, because not only can processes be carried out in parallel, but they do not always have to be followed in a particular order. This paper therefore offers a novel investigation and explanation of RE practices for packaged software implementation, approaching the phenomenon from the viewpoint of the analysts, and offers the first extensive study of packaged software implementation RE (PSIRE) in SMSSDCs.
Bellwether effect refers to the existence of exemplary projects (called the Bellwether) within a historical dataset to be used for improved prediction performance. Recent studies have shown an implicit assumption of using recently completed projects
(referred to as moving window) for improved prediction accuracy. In this paper, we investigate the Bellwether effect on software effort estimation accuracy using moving windows. The existence of the Bellwether was empirically proven based on six postulations. We apply statistical stratification and Markov chain methodology to select the Bellwether moving window. The resulting Bellwether moving window is used to predict the software effort of a new project. Empirical results show that Bellwether effect exist in chronological datasets with a set of exemplary and recently completed projects representing the Bellwether moving window. Result from this study has shown that the use of Bellwether moving window with the Gaussian weighting function significantly improve the prediction accuracy.
Global Software Engineering (GSE) research contains few examples consciously applying what Glass and colleagues have termed an evaluative-critical approach. In this study we apply dilemma analysis to conduct a critical review of a major (and ongoing)
nearshore Business Process Outsourcing project in New Zealand. The project has become so troubled that a Government Minister has recently been assigned responsibility for troubleshooting it. The Novopay project concerns the implementation of a nationwide payroll system responsible for the payment of some 110,000 teachers and education sector staff. An Australian company won the contract for customizing and implementing the Novopay system, taking over from an existing New Zealand service provider. We demonstrate how a modified form of dilemma analysis can be a powerful technique for highlighting risks and stakeholder impacts from empirical data, and that adopting an evaluative-critical approach to such projects can usefully highlight tensions and barriers to satisfactory project outcomes.
