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RPA (Robotic process automation) is an emerging field in software engineering that is applied in a wide variety of industries to automate repetitive business processes. While the tools to create RPA projects have evolved quickly, testing in these projects has not yet received much attention. The purpose of this thesis was to study how the regression testing of RPA projects created using UiPath could be automated while avoiding the following most common pitfalls of test automation projects: unreliability, too high cost, lack of re-usable components and too difficult implementation. An automated regression test suite was created as a case study with UiPath for an existing RPA project that is currently being tested manually. The results imply that UiPath can be used to also create the regression test suite, not just the RPA project. The automated test suite could be used to run all the tests in the regression test suite that is currently run manually. The common test automation project pitfalls were also mostly avoided: the structure of the project can be re-used for other test projects, the project can recover from unexpected errors and the implementation of the tests does not require a high level of programming knowledge. The main challenge proved to be the implementation cost which was increased by the longer then expected test development time. Another finding was that the measures taken to address test automation project pitfalls will likely work only with RPA projects that are simpler or as complex as the sample RPA project. With more complex projects, there will also likely be more challenges with test data creation. As a result, for complex projects, manual regression testing could be a better option.

This Master's Thesis describes one example on how to automatically generate tests for real-time protocol software. Automatic test generation is performed using model-based testing (MBT). In model-based testing, test cases are generated from the behaviour model of the system under test (SUT). This model expresses the requirements of the SUT. Many parameters can be varied and test sequences randomised. In this context, real-time protocol software means a system component of Nokia Siemens Networks (NSN) Long Term Evolution (LTE) base station. This component, named MAC DATA, is the system under test (SUT) in this study. 3GPP has standardised the protocol stack for the LTE eNodeB base station. MAC DATA implements most of the functionality of the Medium Access Control (MAC) and Radio Link Control (RLC) protocols, which are two protocols of the LTE eNodeB. Because complex telecommunication software is discussed here, it is challenging to implement MBT for the MAC DATA system component testing. First, the expected behaviour of a system component has to be modelled. Because it is not smart to model everything, the most relevant system component parts that need to be tested have to be discovered. Also, the most important parameters have to be defined from the huge parameter space. These parameters have to be varied and randomised. With MBT, a vast number of different kind of users can be created, which is not reasonable in manual test design. Generating a very long test case takes only a short computing time. In addition to functional testing, MBT is used in performance and worst-case testing by executing a long test case based on traffic models. MBT has been noticed to be suitable for challenging performance and worst-case testing. This study uses three traffic models: smartphone-dominant, laptop-dominant and mixed. MBT is integrated into continuous integration (CI) system, which automatically runs MBT test case generations and executions overnight. The main advantage of the MBT implementation is the possibility to create different kinds of users and simulate real-life system behaviour. This way, hidden defects can be found from test environment and SUT.

Hybrid Clouds are one of the most notable trends in the current cloud computing paradigm and bare-metal cloud computing is also gaining traction. This has created a demand for hybrid cloud management and abstraction tools. In this thesis I identify shortcomings in Cloudify’s ability to handle generic bare-metal nodes. Cloudify is an open- source vendor agnostic hybrid cloud tool which allows using generic consumer-grade computers as cloud computing resources. It is not however capable to automatically manage joining and parting hosts in the cluster network nor does it retrieve any hardware data from the hosts, making the cluster management arduous and manual. I have designed and implemented a system which automates cluster creation and management and retrieves useful hardware data from hosts. I also perform experiments using the system which validate its correctness, usefulness and expandability.

This study is part of the TEKES funded Electric Brain -project of VTT and University of Helsinki where the goal is to develop novel techniques for automatic big data analysis. In this study we focus on studying potential methods for automated land cover type classification from time series satellite data. Developing techniques to identify different environments would be beneficial in monitoring the effects of natural phenomena, forest fires, development of urbanization or climate change. We tackle the arising classification problem with two approaches; with supervised and unsupervised machine learning methods. From the former category we use a technique called support vector machine (SVM), while from the latter we consider Gaussian mixture model clustering technique and its simpler variant, k-means. We introduce the techniques used in the study in chapter 1 as well as give motivation for the work. The detailed discussion of the data available for this study and the methods used for analysis is presented in chapter 2. In that chapter we also present the simulated data that is created to be a proof of concept for the methods. The obtained results for both the simulated data and the satellite data are presented in chapter 3 and discussed in chapter 4, along with the considerations for possible future works. The obtained results suggest that the support vector machines could be suitable for the task of automated land cover type identification. While clustering methods were not as successful, we were able to obtain as high as 93 % accuracy with the data available for this study with the supervised implementation.

The importance of Automatic Speech Recognition cannot be underestimated in today’s worlds as they play a significant role in human computer interaction. ASR systems have been studied deeply over time, but their maximum potential is yet to be explored for the Finnish language. Development of a traditional ASR system involves a number of hand-crafted engineering which has made this technology quite difficult and resourceful to develop. However, with advancements in the field of neural networks, end-to-end ASR neural networks can be developed which can automatically learn the mappings of audio to its corresponding transcript., therefore reducing hand crafted engineering requirements. End-to-end neural network ASR systems have been largely developed commercially by tech giants such as Microsoft, Google and Amazon. However, there are limitations to these commercial services such as data privacy and cost of usage. In this thesis, we explored existing studies in the development of an end-to-end neural network ASR for Finnish language. One successful technique utilized in the development of neural network ASR in the advent of inadequate data is Transfer learning. This is the approach explored in this thesis for the development of the end-to-end neural network ASR system. In addition, the success of this approach was evaluated. In order to achieve this purpose, dataset collected from the Finnish Bank of Finland and Kaggle were used to fine-tune Mozilla DeepSpeech model which is a pretrained end-to-end neural network ASR in English language. The results obtained by fine-tuning the pretrained neural network ASR in English for Finnish language showed a word error rate as low as 40% and character error rate as low as 22%. We therefore concluded that transfer learning is a successful technique for creating ASR model for a new language using a pretrained model in another language with little effort, data and resources.

In my thesis I look at how persons living without an own car experience their daily mobility and what kind of strategies and practices concerning daily mobility are their households using to manage their daily lives. In focus is also a question of the significance of the place of residence to mobility, which I investigate through the concepts of urban structure and car dependence. I chose the city of Porvoo as my study area, because as a small city it does not have the public transportation services at the level of the largest Finnish cities, but on the other hand its dense city center may support carless daily mobility. I use the concept of accessibility strategies, which means the ways in which individuals can maintain access to the variably time and space bound activities of their everyday lives and overcome or adapt to their time-geographic constraints. Knowing the practices of carless households is important for the targets to reduce greenhouse gas emissions of transportation in Finland. It is still important to remember the nature of carlessness as varying from being voluntary to being involuntary. Urban structure sets the conditions which may favor some travel behavior and prohibit other kinds. These conditions include distances between activity locations, or the relative ease of using different travel modes. Urban structure may enable alternatives in travel mode choices or prohibit them and support mostly private car use. Discussions may also consider car dependence, which has been defined as the dependence on private cars of areas, urban structure, transport systems, as well as individuals and daily trips. As a method for data collection in this qualitative thesis I used thematic interviews. Interviews may help to understand the practices and subjective experiences of a group of people in a certain place, and meanings they attach to an activity of a geographical nature. I interviewed seven persons living in Porvoo, representing their carless households, of which some lived in the city center and others outside of it. I analyzed the interview transcriptions using coding, thematic analysis and typification. Almost all of the interviewees utilized a strategy in which they had taken proximity to daily destinations and activities into consideration when moving to their current place of residence, which enables short distances by walking or cycling. I studied the use of information and communications technologies to substitute physical mobility by looking at remote work practices: high levels of remote work were done, and more than before, when the remote work possibilities were expanded due to COVID-19 pandemic. All of the households had received support for mobility from their social relations, but the significance of this strategy to everyday life varied considerably, from a weekly need of getting car rides to a rare occasion of borrowing a car. Central daily mobility practices were walking and cycling, trip chaining, and choosing activities from a close proximity to home. The daily mobility experiences of households living in Porvoo city center, or its immediate surroundings were characterized as being problem-free. City center’s short distances and bus connections to Helsinki were seen as advantages to mobility. The most pronounced challenges to daily mobility appeared within those living outside of the city center, due to experiences of a decline in the service level of local public transportation. Local buses did not offer satisfying levels of accessibility to those who would have needed them for their daily trips. The finding of the problem-free nature of daily mobility of the ones living in or next to city center is in line with a finding from literature, which sees downtown areas of middle-sized Finnish cities as representing a car independent urban structure.

In nearest neighbors search the task is to find points from a data set that lie close in space to a given query point. To improve on brute force search, that computes distances between the query point and all data points, numerous data structures have been developed. These however perform poorly in high dimensional spaces. To tackle nearest neighbors search in high dimensions it is commonplace to use approximate methods that only return nearest neighbors with high probability. In practice an approximate solution is often as good as an exact one, among other reasons because approximations can be of such a high quality that they are practically indistinguishable from exact solutions. Approximate nearest neighbors search has found applications in many different fields, and can for example be used in the context of recommendation systems. One class of approximate nearest neighbors algorithms is space partitioning methods. These algorithms recursively partition the data set to smaller subsets in order to construct a search structure. Queries can then be performed very efficiently by using this structure to prune data points without needing to evaluate their distances to the query point. A recent proposal belonging to this class of algorithms is multiple random projections trees (MRPT). MRPT uses random projection trees (RP-trees) to prune the set from which nearest neighbors are searched. This thesis proposes a voting algorithm for using multiple RP-trees in nearest neighbors search. We also discuss a further improvement, called mixture method. The performance of these algorithms was evaluated against the previous MRPT algorithm using two moderately high dimensional data sets. Mixture method was found to improve considerably on MRPT in terms of accuracy attained. The results presented in this thesis suggest that the mixture method may potentially be a strong algorithm for nearest neighbors search, especially in very high dimensional spaces.

Koulumatematiikka on ottanut valtavia harppauksia eteenpäin viime vuosien aikana. Vuonna 2014 hyväksytty perusopetuksen opetussuunnitelman perusteet toivat ohjelmoinnin osaksi matematiikan opetust. Opetussuunnitelmassa painotetaan paljon yleisesti sekä matematiikan osalta tieto- ja viestintäteknologian hyödyntämistä osana opetusta. Teknologia antaa runsaasti mahdollisuuksia työskentelyyn koulussa. Avaruusgeometriaa voi luoda, havainnoida tai tutkia siihen tarkoitetuilla sovelluksilla. Valmiiksi tehtyjä käyttökelpoisia appletteja löytyy esimerkiksi GeoGebraltaa hyvin. Tästä voi olettaa, että oppikirjat antavat oppilaille paljon tukea teknologian hyödyntämiseen opinnoissa. Koulussa käytettävät oppimateriaalit muuttuivat viimeisimmän opetussuunnitelman myötä. Esimerkiksi materiaalit voivat olla digitaalisessa muodossa perinteisen kirjan sijaan. Perinteisissä oppikirjoissa on aikaisempien kirjojen tyyliin tehtäviä laidasta laitaan. Avaruusgeometriassa tehtävät painottuvat hahmottamiseen ja laskemiseen, mutta myös kolmiulotteiseen hahmottamiseen liittyviä tehtäviä on jonkin verran. Tieto- ja viestintäteknologiaa ei ole oppilaiden kirjoissa mainittuna tai tuotu esiin. Ylioppilastutkinnossa matematiikka on suoritettu keväästä 2019 alkaen sähköisenä kokeena. Peruskoulusta lukioon jatkaa yli puolet oppilaista, joten yläkoulun jälkeen oppilailla on muutama vuosi lukiossa aikaa omaksua erilaiset sähköiset työkalut. Lukiossa opiskeleville olisivaltavasti etua, jos perusopetuksessa tutustuttaisiin esimerkiksi geometrisiin piirtotyökaluihin. Nivelvaihe peruskoulun ja lukion välillä on joka tapauksessa suuri harppaus. Perusopetuksen matematiikan opetusmateriaaleissa ei ole hyödynnetty teknologiaa avaruusgeometriassa. Lisätutkimus teknologian integroimisesta osaksi matematiikan ja avaruusgeometrian opetusta olisi toivottavaa.

Organisaatioiden liiketoimintajärjestelmät ovat tyypillisesti organisaation toimintaan sopivaksi muokattuja suljettuja kokonaisuuksia, joiden on kuitenkin tarpeen integroitua muihin järjestelmiin. Integraatioalustat tarjoavat malleja ja palveluita, joiden avulla heterogeenisten järjestelmien keskinäistä tiedon ja prosessien jakoa voidaan korkealla tasolla yksinkertaistaa. Tutkielman tarkoituksena on löytää vanhentuneen integraatioalustan rinnalle modernimpi alusta. Vertaamalla olemassaolevaa alustaa tyypillisiin järjestelmäintegraatioissa käytettyihin menetelmiin, arkkitehtuureihin ja suunnittelumalleihin saadaan monien alustojen joukosta valittua yksi (Spring Framework ja sen laajennokset), jota tutkitaan tarkemmin. Käyttäjille suunnatun kyselyn avulla olemassaolevasta alustasta selvinneisiin ongelmakohtiin vertaamalla saadaan uudelle alustalle tehtyä maaliperustaiset vaatimukset, sekä niihin liittyvät metriikat. Alustojen vertailusta saatujen tulosten perusteella uusi alusta täyttää sille asetetut vaatimukset, ja paikkaa olemassaolevan alustan ongelmat.

Tässä tutkimuksessa käsitellään ongelmanratkaisua osana matematiikan opettamista. Aihe on tärkeä, sillä ongelmanratkaisu on taito, jota yksilö tarvitsee elämänsä kaikilla eri osa-alueilla aina arkipäivän ongelmatilanteista työelämän haasteisiin. Tämän tutkimuksen tarkoituksena on antaa matematiikan opettajille eväitä ongelmalähtöisen oppimateriaalin kehittämiseen ja ongelmalähtöisen opetuksen toteuttamiseen. Tutkimuksessa pyritään kehittämään ratkaisu opettajien kokemaan ongelmaan ongelmanratkaisun ja matematiikan sisältöjen opettamisen irrallisuudesta vastaamalla kysymyksiin 'Miten opettaja voi tukea ongelmanratkaisun oppimista?' ja 'Minkälainen oppimateriaali tukee sekä käsitteen oppimista että kehittää oppilaiden ongelmanratkaisukykyä?'. Tutkimusmetodina käytetään kehittämistutkimusta, jossa teoreettisen ongelma-analyysin perusteella kehitetään käyttökelpoinen opetusmateriaali. Tutkimuksen teoreettisen ongelma-analyysin tavoitteena on myös kehittää syvällinen ymmärrys tutkittavasta aihepiiristä. Teoreettisen osion merkittävinä lähteinä ovat toimineet Haapasalon (2011) ja Pehkosen (1991) teokset ja se koostuu ongelmanratkaisun teoriasta sekä ongelmanratkaisun ja funktiokäsitteen oppimisen teoriasta. Teoreettisen ongelma-analyysin perusteella kehitettyä opetusmateriaalia testataan opetuskokeilulla ja sen jatkokehitysmahdollisuuksia pohditaan. Opetuskokeilun tutkimusmenetelmänä on osallistuva havainnointi. Teoreettisen ongelma-analyysin pohjalta oppimistehtävän ensisijaisiksi tavoitteeksi asetettiin oppilaiden ajattelutaitojen kehittäminen sekä opetettavan uuden käsitteen kytkeminen käytännönläheiseen kontekstiin. Oppimistehtävän empiirisessä testauksessa havaittiin tehtävän toteuttavan sille asetetut tavoitteet, mutta lisäksi havaittiin myös mahdollisia jatkokehitystarpeita. Tehtävä onnistui tavoitteessaan käytännönläheisenä johdantona funktioihin, mutta sen muotoiluun avoimuuden osalta voi olla syytä kiinnittää huomiota riippuen oppilasryhmän tasosta. Kirjallisuuskatsauksen perusteella opettajan näkökulmasta ongelmanratkaisun opetus on nähtävissä pitkäaikaisena prosessina, jossa opettajan rooli muuttuu oppilaiden kehittyessä. Aluksi oppilaat tarvitsevat malliesimerkkiä kun he vasta omaksuvat uusia ajatusmalleja. Oppilaiden ongelmanratkaisutaitojen karttuessa opetusta voi muuttaa yhä ongelmalähtöisempään suuntaan ja lopulta heuristiikkojen automatisoiduttua pitkäjänteisen harjoittelun tuloksena, ne voidaan tehdä oppilaille tietoisiksi. Ongelmanratkaisu nähdään yleisesti hyväksyttynä keinona oppilaiden matemaattisen ja kriittisen ajattelun kehityksessä ja sen tulisi olla osa matematiikan opetusta.