Improvement Opportunities and Suggestions for Benchmarking

During the past 10 years, the amount of effort put on setting up benchmarking repositories has considerably increased at the organizational, national and even at international levels to help software managers to determine the performance of software activities and to make better software estimates. This has enabled a number of studies with an emphasis on the relationship between software product size, effort and cost factors in order to either measure the average performance for similar software projects or develop reliable estimation models and then refine them using the collected data. However, despite these efforts, none of those methods are yet deemed to be universally applicable and there is still no agreement on which cost factors are significant in the estimation process. This study discusses some of the possible reasons why in software engineering, practitioners and researchers have not yet been able to come up with well defined relationships between effort and cost drivers although considerable amounts of data on software projects have been collected.Volume 5891/200

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

Digitaliseringens påvirkning på utøvelse av prosessledelse : En kvalitativ eksplorerende studie av hvordan digitalisering påvirker utøvelse av prosessledelse i dagens virksomheter

Denne masterutredningen har som hensikt å ta for seg krysningen mellom prosessledelse og digitalisering. Det eksisterer generelt begrenset med forskning innenfor dette fagfeltet og denne oppgaven har som hensikt å utforske dette videre. Datainnsamlingen for forskningen vår er gjort gjennom ni intervjuer med representanter fra ni forskjellige virksomheter, i ulike bransjer og sektorer. Forskningen vår viser at digitalisering har stor påvirkning på arbeidet med prosessledelse. For det første viser funnene at digitalisering fører til mer komplekse prosesser. Dette har ringvirkninger på hvordan en håndterer prosessene i både planleggings- og gjennomføringsfasen. For det andre viser funn i vår forskning at digitalisering fungerer som et verktøy med flere funksjoner innenfor prosessledelse. Denne rollen som verktøy er med på å muliggjøre standardisering, oppnå etterlevelse, optimalisere samarbeid, og støtte opp mot kartleggingen av prosesser. Samtidig avdekker funnene at digitalisering ikke alltid er roten til de største endringene. Vår masterutredning er et bidrag til forskning på digitaliseringens påvirkning på prosessledelse. Dette studiet kan være med på å bygge en større forståelse for samspillet mellom teknologi og håndtering av prosesser.nhhma

Development of a prototype for multidimensional performance management in software engineering

Managing performance is an important, and difficult, topic, and tools are needed to help organizations manage their performance. Understanding, and improving performance is an important problem. Performance management has become more and more important for organizations, and managers are always on the lookout for better solutions to manage performance within their organizations. One of the most important consequences of not having a Performance Management Framework (PMF) in place is the difficulty of differentiating organizational success from failure over time. Performance Management Frameworks have become important to organizations that need to plan, monitor, control, and improve their decisions. Use of a PMF can show an organization how it is performing and indicate whether or not an organization is going in the right direction to achieve its objectives. Over the years, several frameworks have been developed to address the management of organizational assets, both tangible and intangible. Performance measurement has always mostly been focused on the economic viewpoint. The framework developed by Kaplan and Norton adds three other viewpoints to this, and this addition represents a significant improvement to PMFs. The PMFs currently proposed do not meet the analytical requirements of software engineering management when various viewpoints must be taken into account concurrently. This difficulty is compounded by the fact that the underlying quantitative data are multidimensional, and so the usual two- and three-dimensional approaches to visualization are generally not sufficient to represent such models. Organizations vary considerably in the wide variety of viewpoints that influence their performance, and every organization has their own viewpoints that they want to manage, and which must be represented in a consolidated manner. The purpose of this thesis is to develop a prototype for managing multidimensional performance in software engineering. The thesis begins by defining the important terms or key concepts used in the research: software, performance, management, model, multidimensional, development, engineering, and prototype, and the various associations of these terms. This is followed by a review of the multidimensional PMFs that are specific to software engineering and the generic multidimensional performance models that are available to management. A framework for managing performance in software engineering in four phases: design, implementation, use of the framework, and performance improvement is then presented. Based on this framework, a prototype tool is developed. The prototype notably includes visual analytical tools to manage, interpret, and understand the results in a consolidated manner, while at the same time keeping track of the values of the individual dimensions of performance. The repository of software project data made available by the International Software Benchmarking Standard Group (ISBSG) is integrated into and used by the prototype as well