Self-organising Roles in Agile Globally Distributed Teams

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 exacerbates 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

Data gathering for actor analyses: A research note on the collection and aggregation of individual respondent data for MACTOR

The augmentation of future studies with data on actors and their interactions is suggested as a means to reduce uncertainty and to account for extreme or unexpected future outcomes due to the involvement of multiple actors and their competing perspectives and options. In the context of New Zealand’s health workforce forecasting environment, this research note presents a systematic method to gather and aggregate actor data developed for a recent foresight study. The method identifies the issues encountered and solutions developed when gathering data from time poor respondents representing diverse and sometimes oppositional actors, and for the coding and aggregation of these data for use in LIPSOR’s actor analysis tool, MACTOR. Worked examples are provided to demonstrate the method’s application with the software

Towards a standardised strategy to collect and distribute application software artifacts

Reference sets contain known content that are used to identify relevant or filter irrelevant content. Application profiles are a type of reference set that contain digital artifacts associated with application software. An application profile can be compared against a target data set to identify relevant evidence of application usage in a variety of investigation scenarios. The research objective is to design and implement a standardised strategy to collect and distribute application software artifacts using application profiles. An advanced technique for creating application profiles was designed using a formalised differential analysis strategy. The design was implemented in a live differential forensic analysis tool, LiveDiff, to automate and simplify data collection. A storage mechanism was designed based on a previously standardised forensic data abstraction. The design was implemented in a new data abstraction, Application Profile XML (APXML), to provide storage, distribution and automated processing of collected artifacts

Experience: Quality benchmarking of datasets used in software effort estimation

Data is a cornerstone of empirical software engineering (ESE) research and practice. Data underpin numerous process and project management activities, including the estimation of development effort and the prediction of the likely location and severity of defects in code. Serious questions have been raised, however, over the quality of the data used in ESE. Data quality problems caused by noise, outliers, and incompleteness have been noted as being especially prevalent. Other quality issues, although also potentially important, have received less attention. In this study, we assess the quality of 13 datasets that have been used extensively in research on software effort estimation. The quality issues considered in this article draw on a taxonomy that we published previously based on a systematic mapping of data quality issues in ESE. Our contributions are as follows: (1) an evaluation of the “fitness for purpose” of these commonly used datasets and (2) an assessment of the utility of the taxonomy in terms of dataset benchmarking. We also propose a template that could be used to both improve the ESE data collection/submission process and to evaluate other such datasets, contributing to enhanced awareness of data quality issues in the ESE community and, in time, the availability and use of higher-quality datasets