Bayesian Hierarchical Modelling for Tailoring Metric Thresholds

Software is highly contextual. While there are cross-cutting `global' lessons, individual software projects exhibit many `local' properties. This data heterogeneity makes drawing local conclusions from global data dangerous. A key research challenge is to construct locally accurate prediction models that are informed by global characteristics and data volumes. Previous work has tackled this problem using clustering and transfer learning approaches, which identify locally similar characteristics. This paper applies a simpler approach known as Bayesian hierarchical modeling. We show that hierarchical modeling supports cross-project comparisons, while preserving local context. To demonstrate the approach, we conduct a conceptual replication of an existing study on setting software metrics thresholds. Our emerging results show our hierarchical model reduces model prediction error compared to a global approach by up to 50%.Comment: Short paper, published at MSR '18: 15th International Conference on Mining Software Repositories May 28--29, 2018, Gothenburg, Swede

Is My Project's Truck Factor Low? Theoretical and Empirical Considerations About the Truck Factor Threshold

The Truck Factor is a simple way, proposed by the agile community, to measure the system's knowledge distribution in a team of developers. It can be used to highlight potential project problems due to the inadequate distribution of the system knowledge. Notwithstanding its relevance, only few studies investigated the Truck Factor and proposed ways to efficiently measure, evaluate and use it. In particular, the effective use of the Truck Factor is limited by the lack of reliable thresholds. In this preliminary paper, we present a theoretical model concerning the Truck Factor and, in particular, we investigate its use to define the maximum achievable Truck Factor value in a project. The relevance of such a value concerns the definition of a reliable threshold for the Truck Factor. Furthermore in the paper, we document an experiment in which we apply the proposed model to real software projects with the aim of comparing the maximum achievable value of the Truck Factor with the unique threshold proposed in literature. The preliminary outcome we achieved shows that the existing threshold has some limitations and problem

Techniques for calculating software product metrics threshold values: A systematic mapping study

Several aspects of software product quality can be assessed and measured using product metrics. Without software metric threshold values, it is difficult to evaluate different aspects of quality. To this end, the interest in research studies that focus on identifying and deriving threshold values is growing, given the advantage of applying software metric threshold values to evaluate various software projects during their software development life cycle phases. The aim of this paper is to systematically investigate research on software metric threshold calculation techniques. In this study, electronic databases were systematically searched for relevant papers; 45 publications were selected based on inclusion/exclusion criteria, and research questions were answered. The results demonstrate the following important characteristics of studies: (a) both empirical and theoretical studies were conducted, a majority of which depends on empirical analysis; (b) the majority of papers apply statistical techniques to derive object-oriented metrics threshold values; (c) Chidamber and Kemerer (CK) metrics were studied in most of the papers, and are widely used to assess the quality of software systems; and (d) there is a considerable number of studies that have not validated metric threshold values in terms of quality attributes. From both the academic and practitioner points of view, the results of this review present a catalog and body of knowledge on metric threshold calculation techniques. The results set new research directions, such as conducting mixed studies on statistical and quality-related studies, studying an extensive number of metrics and studying interactions among metrics, studying more quality attributes, and considering multivariate threshold derivation. 2021 by the authors. Licensee MDPI, Basel, Switzerland.Funding: Authors thanks to the Molde University College-Specialized Univ. in Logistics, Norway for the support of Open access fund.Scopus2-s2.0-8512089773

Are comprehensive quality models necessary for evaluating software quality?

The concept of software quality is very complex and has many facets. Reflecting all these facets and at the same time measuring everything related to these facets results in comprehensive but large quality models and extensive measurements. In contrast, there are also many smaller, focused quality models claiming to evaluate quality with few measures. We investigate if and to what extent it is possible to build a focused quality model with similar evaluation results as a comprehensive quality model but with far less measures needed to be collected and, hence, reduced effort. We make quality evaluations with the comprehensive Quamoco base quality model and build focused quality models based on the same set of measures and data from over 2,000 open source systems. We analyse the ability of the focused model to predict the results of the Quamoco model by comparing them with a random predictor as a baseline. We calculate the standardised accuracy measure SA and effect sizes. We found that for the Quamoco model and its 378 automatically collected measures, we can build a focused model with only 10 measures but an accuracy of 61% and a medium to high effect size. We conclude that we can build focused quality models to get an impression of a system’s quality similar to comprehensive models. However, when including manually collected measures, the accuracy of the models stayed below 50%. Hence, manual measures seem to have a high impact and should therefore not be ignored in a focused model

Konzeption und Realisierung eines Sleep Monitoring Frameworks für iOS

Tinnitus ist eine häufig auftretende Gesundheitsstörung, deren Ursache immer noch unklar ist. Etwa 1% der Bevölkerung leidet unter diesem Problem. Mittels dieser Arbeit soll für die Tinnitusforschung ein Framework zur Verfügung gestellt werden, um Daten von Patienten während des Schlafens erheben zu können. Dabei kommt ein iPhone zum Einsatz, mit dessen Sensoren Bewegungen und Geräusche aufgezeichnet werden. Im Anschluss daran werden die ermittelten Daten aufbereitet und analysiert. Die daraus gewonnen Erkenntnisse stehen dann der eingesetzten mobilen Anwendung zur Verfügung. Im Zuge dieser Bachelorarbeit wird beispielhaft eine Anwendung umgesetzt, die den Einsatz des Frameworks ermöglicht und die Ergebnisse der analysierten Daten präsentiert. Die Anwendung und das Framework sind allgemein für iOS-basierte Plattformen entwickelt