ABSTRACT This essay is about traffic analysis of Web servers. The purpose is to investigate if companies analyse the traffic on the Web server and if they use this information to create more than simple statistic reports. The questions that we will answer are the following: ? How common is traffic analysis on the Web server among companies? ? Is the Web administrator limited in analysing the Web traffic, if the company has the website on a Web hotel? ? Does the company uses the results they get from analysing the traffic on the Web server for updates and changes on the Web page? To answer these questions we made a questionnaire investigation by telephone interviewing the companies? webmasters. The extent of the investigation was 20 companies in Blekinge. The information we gathered from the investigation showed that it is becoming more common that companies analyse the traffic on the Web page and that they uses the information to more than statistic reports, for example updates and changes of the Web page. Based on the investigation we did a statistical comparison between the companies which analysed their traffic and the companies which not analysed their traffic on the Web server. We compared the turnover?s growth for the last four years. From the results of the comparison we could not draw any statistical conclusions that the traffic analysis can have any direct influence on the company?s business activity, but we may discern a trend that the companies, which analyse the traffic, had a stronger growth of the turnover. Because of these conclusions we couldn?t confirm the hypothesis as follows: ?The information that you can extract by analyse the traffic on the Web server can be used to improve the business activity of companies.? Our essay has however led us to gather some trends. The analysing tools have developed essentially the last years, but it?s hard to se that analyse has a direct influence on the business activity, which implies that our hypothesis is false. The development of analysing tools where more than one data source is connected to traffic analysing is probably going to make it easier to se the influence on the business activity in the future.

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.