In 2000, the National Aeronautics and Space Administration (NASA) initiated a program to promote the use of small aircraft as an additional option for national public transportation. The Small Aircraft Transportation System (SATS) asserted the idea of everyday individuals piloting themselves on trips, within a specified distance range, using a small (4 person), piston powered, un-pressurized aircraft and small airports in close proximity to their origin and destination. This thesis investigates how one weather phenomenon, in-flight icing, affects the dispatch reliability of this transportation system. Specifically, this research presumes that a route is considered a "no-go" for low time pilots in a small, piston powered aircraft if any icing conditions are forecast along the route at the altitude of the flight during the time the traveler desires to make the trip. This thesis evaluates direct flights between Cleveland and Boston; Boston and Washington, D.C.; and Washington, D.C. and Cleveland during the months of November through May for the years 2001 to 2003 at maximum cruising altitudes of 6,000 feet, 8,000 feet, 10,000 feet, and 12,000 feet above mean sea level (MSL). It was found that the overall probability of a "no-go" for all three flight paths at the normal cruising altitude of 12,000 feet is 56.8%. When the cruising altitude is reduced to 10,000 feet, 8,000 feet, and 6,000 feet the probability of a "no-go" for all three flight paths reduces to 54.6%, 48.5%, and 43.7% respectively. / Master of Science

This master thesis addresses the problem of classifying test failures in Ericsson AB’s BAIT test framework, specifically distinguishing between environment faults and product faults. The project aims to automate the initial defect classification process, reducing manual work and facilitating faster debugging. The significance of this problem lies in the potential time and cost savings it offers to Ericsson and other companies utilizing similar test frameworks. By automating the classification of test failures, developers can quickly identify the root cause of an issue and take appropriate action, leading to improved efficiency and productivity. To solve this problem, the thesis employs machine learning techniques. A dataset of test logs is utilized to evaluate the performance of six classification models: logistic regression, support vector machines, k-nearest neighbors, naive Bayes, decision trees, and XGBoost. Precision and macro F1 scores are used as evaluation metrics to assess the models’ performance. The results demonstrate that all models perform well in classifying test failures, achieving high precision values and macro F1 scores. The decision tree and XGBoost models exhibit perfect precision scores for product faults, while the naive Bayes model achieves the highest macro F1 score. These findings highlight the effectiveness of machine learning in accurately distinguishing between environment faults and product faults within the Bait framework. Developers and organizations can benefit from the automated defect classification system, reducing manual effort and expediting the debugging process. The successful application of machine learning in this context opens up opportunities for further research and development in automated defect classification algorithms. / Detta examensarbete tar upp problemet med att klassificera testfel i Ericsson AB:s BAIT-testramverk, där man specifikt skiljer mellan miljöfel och produktfel. Projektet syftar till att automatisera den initiala defekten klassificeringsprocessen, vilket minskar manuellt arbete och underlättar snabbare felsökning. Betydelsen av detta problem ligger i de potentiella tids- och kostnadsbesparingarna det erbjuder till Ericsson och andra företag som använder liknande testramar. Förbi automatisera klassificeringen av testfel, kan utvecklare snabbt identifiera grundorsaken till ett problem och vidta lämpliga åtgärder, vilket leder till förbättrad effektivitet och produktivitet. För att lösa detta problem använder avhandlingen maskininlärningstekniker. A datauppsättning av testloggar används för att utvärdera prestandan för sex klassificeringar modeller: logistisk regression, stödvektormaskiner, k-närmaste grannar, naiva Bayes, beslutsträd och XGBoost. Precision och makro F1 poäng används som utvärderingsmått för att bedöma modellernas prestanda. Resultaten visar att alla modeller presterar bra i klassificeringstest misslyckanden, uppnå höga precisionsvärden och makro F1-poäng. Beslutet tree- och XGBoost-modeller uppvisar perfekta precision-spoäng för produktfel, medan den naiva Bayes-modellen uppnår högsta makro F1-poäng. Dessa resultat belyser effektiviteten av maskininlärning när det gäller att exakt särskilja mellan miljöfel och produktfel inom Bait-ramverket. Utvecklare och organisationer kan dra nytta av den automatiska defektklassificeringen system, vilket minskar manuell ansträngning och påskyndar felsöknings-processen. De framgångsrik tillämpning av maskininlärning i detta sammanhang öppnar möjligheter för vidare forskning och utveckling inom automatiserade defektklassificeringsalgoritmer.