Network usage is an important quality metric for mobile apps. Slow networks, low monthly traffic quotas and high roaming fees restrict mobile users’ amount of usable Internet traffic. Companies wanting their apps to stay competitive must be aware of their network usage and changes to it. Short feedback loops for the impact of code changes are key in agile software development. To notify stakeholders of changes when they happen without being prohibitively expensive in terms of manpower the change detection must be fully automated. To further decrease the manpower overhead cost of implementing network usage change detection the system need to have low configuration requirements, and keep the false positive rate low while managing to detect larger changes. This thesis proposes an automated change detection method for network activity to quickly notify stakeholders with relevant information to begin a root cause analysis after a change in the network activity is introduced. With measurements of the Spotify’s iOS app we show that the tool achieves a low rate of false positives while detecting relevant changes in the network activity even for apps with dynamic network usage patterns as Spotify. / Nätverksaktivitet är ett viktigt kvalitetsmått för mobilappar. Mobilanvändare begränsas ofta av långsamma nätverk, låg månatlig trafikkvot och höga roamingavgifter. Företag som vill ha konkurrenskraftiga appar behöver vara medveten om deras nätverksaktivitet och förändringar av den. Snabb återkoppling för effekten av kodändringar är vitalt för agil programutveckling. För att underrätta intressenter om ändringar när de händer utan att vara avskräckande dyrt med avseende på arbetskraft måste ändringsdetekteringen vara fullständigt automatiserad. För att ytterligare minska arbetskostnaderna för ändringsdetektering av nätverksaktivitet måste detekteringssystemet vara snabbt att konfigurera, hålla en låg grad av felaktig detektering samtidigt som den lyckas identifiera stora ändringar. Den här uppsatsen föreslår ett automatiserat förändringsdetekteringsverktyg för nätverksaktivitet för att snabbt meddela stakeholders med relevant information för påbörjan av grundorsaksanalys när en ändring som påverkar nätverksaktiviteten introduceras. Med hjälp av mätningar på Spotifys iOS-app visar vi att verktyget når en låg grad av felaktiga detekteringar medan den identifierar ändringar i nätverksaktiviteten även för appar med så dynamisk nätverksanvändning som Spotify.

CHARLES UNIVERSITY IN PRAGUE Faculty of Arts Institute of Information Studies and Librarianship February 2012 DISERTATION THESIS ABSTRACT Information Systems Quality Measurement and Evaluation This dissertation thesis includes a concept of an information system evaluation based on the criteria provided with respect to the philosophical, historical and cultural context and using contemporary methodologies well proven in industrial quality assurance systems. The reasoning and proper foundations are made with the sampling methodology through the selected sources interpretation. A comprehensive and an integral approach apply paradigms inspired with for example Aristotle, Giordano Bruno, Confucius and also with the contemporary authorities in philosophy and arts (Belohradsky), information science (Wiener, Shannon, Nielsen), management (Ducker), economy (Stieglitz), psychology and sociology (Kohoutek, Koukolik), quality assurance systems and software and information technology quality assurance systems based on the CSN, EN, ISO and IEC standards. The essence and the nature of information are put to the relation with a value in its social, economic and ethic sense. Based on the information systems quality model designed on such basis, using the statistic methodologies and the seven year data retention pool, the...

Context: The core of software testing is test artifacts, i.e., test cases, test suites, test scripts, test code, test specifications, and natural language tests. Hence, the quality of test artifacts can negatively or positively impact the reliability of the software testing process. Several empirical studies and secondary studies have investigated the test artifact quality. Nevertheless, little is known about how practitioners by themselves perceive test artifact quality, and the evidence on test artifact quality in the literature has not been synthesized in one place. Objective: This thesis aims to identify and synthesize the knowledge in test artifact quality from both academia and industry. Hence, our objectives are: (1) To understand practitioners’ perspectives on test artifact quality, (2) To investigate how test artifact quality has been characterized in the literature, (3) To increase the reliability of the research method for conducting systematic literature reviews (SLR) in software engineering. Method: In this thesis, we conducted an interview-based exploratory study and a tertiary study to achieve the first two objectives. We used the tertiary study as a case and referred to related observations from other researchers to achieve the last objective. Results: We provided two quality models based on the findings of the interview-based and tertiary studies. The two models were synthesized and combined to provide a broader view of test artifact quality. Also, the context information that can be used to characterize the environment in which test artifact quality is investigated was aggregated based on these studies’ findings. Based on our experience in constructing and validating automated search results using a quality gold standard (QGS) in the tertiary study, we provided recommendations for the QGS construction and proposed an extension to the current search validation approach. Conclusions: The context information and the combined quality model provide a comprehensive view of test artifact quality. Researchers can use the quality model to develop guidelines, templates for designing new test artifacts, or assessment tools for evaluating existing test artifacts. The model also can serve as a guideline for practitioners to search for test-artifact quality information, i.e., definitions for the quality attributes and measurements. For future work, we aim at investigating how to improve relevant test artifact quality attributes that are challenging to deal with.

The problem of quality assurance/quality control (QA/QC) for real-time measurements of environmental and water quality variables has been a field explored by many in recent years. The use of in situ sensors has become a common practice for acquiring real-time measurements that provide the basis for important natural resources management decisions. However, these sensors are susceptible to failure due to such things as human factors, lack of necessary maintenance, flaws on the transmission line or any part of the sensor, and unexpected changes in the sensors' surrounding conditions. Two types of machine learning techniques were used in this study to assess the detection of anomalous data points on turbidity readings from the Paradise site on the Little Bear River, in northern Utah: Artificial Neural Networks (ANNs) and Relevance Vector Machines (RVMs). ANN and RVM techniques were used to develop regression models capable of predicting upcoming Paradise site turbidity measurements and estimating confidence intervals associated with those predictions, to be later used to determine if a real measurement is an anomaly. Three cases were identified as important to evaluate as possible inputs for the regression models created: (1) only the reported values from the sensor from previous time steps, (2) reported values from the sensor from previous time steps and values of other water types of sensors from the same site as the target sensor, and (3) adding as inputs the previous readings from sensors from upstream sites. The decision of which of the models performed the best was made based on each model's ability to detect anomalous data points that were identified in a QA/QC analysis that was manually performed by a human technician. False positive and false negative rates for a range of confidence intervals were used as the measure of performance of the models. The RVM models were able to detect more anomalous points within narrower confidence intervals than the ANN models. At the same time, it was shown that incorporating as inputs measurements from other sensors at the same site as well as measurements from upstream sites can improve the performance of the models.

Thesis advisor: Zhushan Li / Educational public private partnerships (PPP), referring to the shared delivery of education services by the government and private providers, have been increasing in recent decades, especially in low- and middle-income countries (LMIC). Yet to date, there has been limited research on their role in the education landscape, in part due to the difficulty of classifying PPP schools in large-scale datasets, which typically classify schools as either public or private. In addition, few studies have assessed PPPs and school quality assurance indicators typically associated with them.  The study had two purposes. First, to explore the possibility of classifying PPP schools in a large-scale dataset using a statistical method. And second, to use these classifications to examine the differences between PPP, public, and private schools on school quality assurance measures, including but not limited to achievement. These analyses were performed using data from the Program for International Student Assessment (PISA), with schools from six of the global emerging economy countries: Brazil, China, Indonesia, Mexico, Russia and Turkey.  Schools were classified using a two-step clustering method using funding and management variables. This revealed three good-quality clusters with a silhouette measure of cohesion and separation of 0.6 (IBM, 2015b; Wendler & Gröttrup, 2016). These were classified as public, private, and PPP based on the characteristics of each school type. With these classifications, the study assessed the relationship between school type and achievement in mathematics, science and reading, and 24 school quality assurance measures from PISA. The analyses controlled for school resources and socio-economic and cultural status. The study found that overall, PPP schools performed better than public schools on three indicators, and better than private schools on five indicators; public schools performed better than PPP schools on one outcome and better than private schools on three outcomes, although with mostly small effect sizes. Private schools did not outperform other school types on any outcome. A country wise analysis showed that these results differed by country. The study highlights the possibility of using two-step clustering to identify PPP schools, the effects of shared funding and management on school performance, and the importance of context in examining countries’ education policies. / Thesis (PhD) — Boston College, 2021. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.

The stability of rock masses is influenced by the occurrence of rock joints. Therefore, the shear strength of rock joints must be considered in dimensioning of underground constructions. One way to predict the shear strength is through usage of failure criteria, which are validated from results of direct shear tests under controlled laboratory conditions. Consequently, the quality of the results from the tests are crucial to the accuracy with which the criteria will be able to predict the shear strength. Since rock joints are unique by nature usage of replicas (man-made copies of rock joints) is of importance in parameter studies. The overall objective of this work is to facilitate the development of improved criteria for predictions of the shear strength of rock joints. To support this objective, two sources of uncertainty have been investigated, namely the geometry of replicas and the influence of the normal stiffness of test systems. Two quality assurance parameters for evaluation of geometrical differences between replicas and rock joints based on scanning data have been derived. The first parameter describes the morphological deviations. The second parameter describes the deviations in orientation with respect to the shear plane. The effective normal stiffness approach, which compensates for the influence of the normal stiffness of the test system in direct shear testing, has been developed, validated, and applied. With help of the quality assurance parameters it is demonstrated that it is possible to reproduce replicas within narrow tolerances. Application of the effective normal stiffness approach basically eliminates the normal load error. In all, the results support generation of improved quality of test data and consequently, the development of shear strength criteria with improved accuracy will also be facilitated. / <p>Academic Dissertation which, with due permission of the KTH Royal Institute of  Technology, is submitted for public defence for the Degree of Licentiate of Engineering on Wednesday the 9th June 2021, at 9:00 a.m. in M108, Brinellvägen 23, Stockholm.</p><p><strong>Paper A</strong>: Larsson J, Flansbjer M, Portal N W, Johnson E, Johansson F, and Mas Ivars D. (2020) Geometrical Quality Assurance of Rock Joint Replicas in Shear Tests – Introductory Analysis. Paper presented at the ISRM International Symposium - EUROCK 2020, physical event not held. https://onepetro.org/ISRMEUROCK/proceedings-abstract/EUROCK20/All-EUROCK20/ISRM-EUROCK-2020-101/451187  In Diva: http://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-51987 </p><p><strong>Paper B:</strong> Larsson J, Johansson F, Mas Ivars D, Johnson E, Flansbjer M and Portal N W. (2021) Rock joint replicas in direct shear testing – Part 1: Extraction of geometrical quality assurance parameters. To be submitted to Rock Mechanics and Rock Engineering  In DiVA: http://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-53111 </p><p><strong>Paper C:</strong> Larsson J and Flansbjer M. (2020) An Approach to Compensate for the Influence of the System Normal Stiffness in CNS Direct Shear Tests. Rock Mechancis and Rock Engineering 53, 2185–2199 https://doi.org/10.1007/s00603-020-02051-0  In DiVA: http://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-44085  </p><p><strong>Paper D:</strong> Larsson J. (2021) Experimental investigation of the system normal stiffness of a 5 MN direct shear test setup and the compensation of it in CNS direct shear tests. Submitted to ISRM International Symposium - EUROCK 2021  In DiVA: http://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-53112 </p>

Using a Research through Design approach, this research aims to utilize the criteria of Flow outlined by Csikszentmihályi in Flow: The Psychology of Optimal Experience (2014) and later adapted to videogames by Sweetser &amp; Wyeth in GameFlow: a model for evaluating player enjoyment in games (2005) to improve the Tower Defense game Daidala by increasing players’ time spent in a state of Flow. This serves to provide future game designers with an example of how this model and these concepts can be applied when designing and developing a game. The research was conducted by exposing players to the game as it came out of development, collecting data from semi-structured interviews and utilizing said data to construct another iteration of the game with the goal of improving players’ experience by increasing the time spent in a Flow mental state. Lastly, this iteration was tested and the results were compared to the previous iteration to determine if the changes succeeded in extending the time players spent in a state of Flow. Our findings show that the GameFlow model by Sweetser &amp; Wyeth (2005) is a powerful tool for game designers looking to improve the experience of the player base of their game through the informed use of Flow. The model provides a solid framework for collecting and evaluating data to guide the process of iteration towards increasing time spent by players in a state of Flow.

The purpose of this study was to examine students’ perceptions of quality of online courses as they relate to the IHEP Teaching/Learning Process Benchmarks, Course Structure Benchmarks, and Student Support Benchmarks. The research design for this study was correlational. Out of 3,310 students enrolled in online courses, 97 successfully participated in the study at an urban southeastern university. A survey instrument consisting of three parts was used in this study. Part I of the survey instrument contains 24 Likert Scale questions, examining students’ online learning experiences. Part II of the survey contained four Likert Scale questions, gathering students’ rating on the overall quality of the online course in which they were enrolled. Part III of the survey instrument sought to obtain demographic information from the participants. The research questions were designed to determine students’ perceptions of online course quality distance education and whether a relationship exists between online students’ perception of quality and the operational variables: peer interaction, instructor feedback, course structure, and student support services. Pearson’s r correlation was used to analyze the survey data. After the data were collected and analyzed, the researcher determined that peer interactions, feedback from the instructors, and course structure contribute to predicting students’ perceptions of online course quality in the online courses studied. Students rated the quality of online teaching, online learning and course structure above average to excellent. Students’ perceptions of the quality of student support services were rated the lowest. An examination of the relationship between student perception of course quality and, instructor feedback, peer interaction, course structure, and student support services was also conducted. The results of the examination indicated that moderately statistically significant relationships existed between student perceptions of course quality and each of the three variables. Conclusions and recommendations based on the findings in this study indicated peer interaction, instructor feedback course structures, and student support services contribute to students’ perceptions of online course quality.

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