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Software has become a key component in spacecraft development. Due to the size and complexity of spacecraft development projects, multiple project stakeholders need to coordinate and exchange development data that must be integrated to provide a big picture. In this context, metrics as for instance defined in the standards such as the European Cooperation for Space Standardization (ECSS) are important. To provide benefits, transparency and clear information regarding the selection and use of software metrics is critical. A lack of transparency and comprehensibility of the process for selecting and collecting key figures by the actors can lead to risks, e.g., important data is not collected or is collected incorrectly. In this paper, to support the streamlined AENEAS measurement tool chain, we developed a tool that helps manage and configure measurement packages that include agreed metrics to be collected in spacecraft development projects. To evaluate the tool, we populated it with 43 metrics from the ECSS-Q-HB-80-04A standard and used it in a case study within a student project. The case study revealed that while the tool facilitated the selection and definition of project metrics, its overall usefulness and clarity received mixed reviews, indicating the need for further improvements.
Over the years, a substantial body of knowledge of software process improvement (SPI) was accumulated that, among other things, includes numerous success factors that companies should consider when conducting improvement activities. The number of success factors is large and quite often, multiple success factors with similar names and descriptions are available to address a specific phenomenon. This raises the question whether all the success factors are unique and, if not, which ones are actually the same. In this paper, we aim to structure the body of knowledge on success factors in SPI. We conducted a systematic literature review on 103 publications that mention 1.320 success factors. A multi-staged manual and AI-supported analysis reduced the number of success factors to 124, which we categorize into 42 general success factor classes. For 20 of these general success factor classes, we observed a stable number of publications over a period of almost 30 years that, however, show only few success factors constantly studied and re-discovered. A high number of synonyms shows that this area of SPI needs consolidation for which we lay the foundation by providing a big picture and identifying the most relevant success factors as a starting point.
With the emergence of agile software development methods, new approaches for determining agile maturity have become necessary. Other than for traditional maturity and capability models like CMMI and ISO/IEC 15504, the field of agile maturity models is not yet settled. Even worse, a common understanding regarding agility in general and the levels of agility in particular is missing. The paper at hand aims to shed light on the field of agile maturity models with a particular focus on maturity levels, their definition, and their evaluation and computation. We conducted a systematic literature review to extract maturity levels and provide an initial harmonization of the levels found. Our findings from analyzing 19 agile maturity models show that there is yet no agreement with regard to the maturity levels. In total, 69 maturity levels have been analyzed for harmonization opportunities. Two major dimensions of maturity levels of agile maturity models could be identified: (1) team-related and (2) general maturity, which is comparable to standard approaches. However, the procedures to assess organizations and processes, if at all present, are to a large extent focused on persons and their personal opinion, which paves the way for future research, e.g., in terms of developing measurement systems for assessing agile maturity.