Browsing by master's degree program "Magisterprogrammet i datavetenskap"

This master's thesis describes the development and validation of a uniform control interface for drawing robots with ROS2. The robot control software was tasked with taking SVG images as input and producing them as drawings with three different robots. These robots are the Evil Mad Scientist AxiDraw V3/A3, UFACTORY xArm Lite6, and virtual xArm Lite6. The intended use case for the robots and companion control software is experiments studying human perception of the creativity of the drawing robots. The control software was implemented over the course of a little over six months and used a combination of C++ and Python. The design of the software utilizes ROS2 abstractions such as nodes and topics to combine different components of the software. The control software is validated against the given requirements and found to fulfil the main objectives of the project. The most important of these are that the robots successfully draw SVG images, that they do so in a similar time frame, and that these images look very similar. Drawing similarity was tested by scanning images, aligning them using using minimal error, and then comparing them visually after overlaying the images. Comparing aligned images was useful in detecting subtle differences in the drawing similarity of the robots and was used to discover issues with the robot control software. MSE and SSIM were also calculated for a set of these aligned images, allowing for the effect of future changes made to the robot control software to be quantitatively evaluated. Drawing time for the robots was evaluated by measuring the time taken for drawing a set of images. This testing showed that the Axidraw's velocity and acceleration needed to be reduced by 56% so that the xArm Lite6 could draw in similar time.

Machine Learning Operations (MLOps) emerged as a practice for applying DevOps practices and culture for machine learning (ML) systems to increase the speed and reliability of deployments. These practices include advocating for automation and monitoring at all steps of the ML system construction, including integration, testing, deployment, and infrastructure management. In addition to continuous integration (CI) and continuous delivery (CD), MLOps introduces continuous training (CT), which is unique to ML systems and is concerned with automatically training and serving ML models. Operating ML systems in production requires continuously adapting to the evolving input data. This is especially evident in time series data, which can experience frequent drifts. Moreover, implementing CT in practice is challenging and heavily dependent on the task and available data. Depending on the complexity of the model and the amount of data, the training process can be computationally costly. Using a scheduled interval for retraining is inefficient if the model still performs adequately. We designed an ML pipeline capable of efficient continuous training using an error-based trigger for retraining the model. The ML pipeline is designed for a time series forecasting task, where the data is prone to frequent drifts. We applied the design science research methodology to identify the problem, design and develop a solution artifact, and evaluate its utility and efficacy. The resulting solution utilizes an open-source MLOps platform that runs on Kubernetes. The solution includes a custom retrainer component to enable CT. We demonstrated the efficacy of the solution using real energy demand data from a university property in Finland. Our evaluation shows that the system is capable of efficient continuous training.

This thesis presents a scalable method for identifying anomalous periods of non-activity in short periodic event sequences. The method is tested with real world point-of-sale (POS) data from grocery retail setting. However, the method can be applied also to other problem domains which produce similar sequential data. The proposed method models the underlying event sequence as a non-homogeneous Poisson process with a piecewise constant rate function. The rate function for the piecewise homogeneous Poisson process can be estimated with a change point detection algorithm that minimises a cost function consisting of the negative Poisson log-likelihood and a penalty term that is linear to the number of change points. The resulting model can be queried for anomalously long periods of time with no events, i.e., waiting times, by defining a threshold below which the waiting time observations are deemed anomalies. The first experimental part of the thesis focuses on model selection, i.e., in finding a penalty value that results in the change point detection algorithm detecting the true changes in the intensity of the arrivals of the events while not reacting to random fluctuations in the data. In the second experimental part the performance of the anomaly detection methodology is measured against stock-out data, which gives an approximate ground truth for the termination of a POS event sequence. The performance of the anomaly detector is found to be subpar in terms of precision and recall, i.e., the true positive rate and the positive predictive value. The number of false positives remains high even with small threshold values. This needs to be taken into account when considering applying the anomaly detection procedure in practice. Nevertheless, the methodology may have practical value in the retail setting, e.g., in guiding the store personnel where to focus their resources in ensuring the availability of the products.

The concept of digital twins, proposed over a decade ago, has recently gathered increasing attention from both industry and academia. Digital twins are real-time or near-real-time simulations of their physical counterparts and can be implemented across various sectors. In mobile networks, digital twins are valuable for maintenance, long-term planning, and expansion by simulating the effects of new infrastructure and technology upgrades. This capability enables network operators to make informed investment and growth decisions. Challenges in implementing digital twins for mobile networks include resource limitations on mobile devices and scaling the system to a broader level. This thesis introduces a modular and flexible architecture for representing network signals from mobile devices within a digital twin environment. It also proposes a suitable platform for digital twins of mobile network signals and resource-efficient protocols for data transmission. The focus is on developing solutions that ensure scalable and resource-efficient synchronization of real or near-real-time data between digital twins and their physical counterparts. The architecture was evaluated through performance testing in two setups: one where data preprocessing occurs on the devices, and another where preprocessing is entirely offloaded to the digital twin platform. Additionally, scalability was assessed by analyzing the platform's ability to handle connections and data transfer from multiple devices simultaneously. The results demonstrate the system's effectiveness and scalability, providing insights into its practical application in real-world scenarios. These findings underscore the potential for widespread adoption and further development of digital twin technologies in mobile networks.

DevOps software development methodologies have steadily gained ground over the past 15 years. Properly implemented DevOps enables the software to be integrated and deployed at a rapid pace. The implementation of DevOps practices create pressure for software testing. In the world of fast-paced integrations and deployments, software testing must perform its quality assurance function quickly and efficiently. The goal of this thesis was to identify the most relevant DevOps software testing practices and their impact on software testing. Software testing in general is a widely studied topic. This thesis looks into the recent develop- ments of software testing in DevOps. The primary sources of this study consist of 15 academic papers, which were collected with the systematic literature review source collection methodolo- gies. The study combines both systematic literature review and rapid review methodologies. The DevOps software testing practices associated with high level of automation, continuous testing and DevOps culture adoption stood out in the results. These were followed by the practices highlighting the need for flexible and versatile test tooling and test infrastructures. DevOps adoption requires the team composition and responsibilities to be carefully planned. The selected testing practices should be carefully chosen. Software testing should be primarily organized in highly automated DevOps pipelines. Manual testing should be utilized to validate the results of the automatic tests. Continuous testing, multiple testing levels and versatile test tooling should be utilized. Integration and regression testing should be run on all code changes. Application monitoring and the collection of telemetry data should be utilized to improve the tests.

Digitaalinen transformaatio tarkoittaa prosessia, jossa organisaatiot reagoivat ympäristössään tapahtuviin muutoksiin käyttämällä uusia digitaalisia teknologioita muuttamaan arvonluonti-prosessejaan pysyäkseen kilpailukykyisinä. Tämän hetken digitaalisen transformaation tärkeimpinä teknologisina ajureina pidetään tekoälyä, lohkoketjua, pilviteknologiaa ja data-analytiikkaa. Tutkielman tarkoituksena oli selvittää kirjallisuuden avulla, miten digitaalinen transformaatio vaikuttaa ohjelmistokehitykseen. Menetelmänä käytettiin systemaattista kirjallisuuskatsausta, sillä se tarjoaa protokollan, jota noudattamalla voidaan vähentää katsaukseen liittyviä vinoumia. Tulosten mukaan digitaalinen transformaatio tarkoittaa ohjelmistokehityksen kannalta eri alojen digitaalisen transformaation toteuttamista, uusien teknologioiden mukaantuloa ja automaation lisääntymistä. Digitaalinen transformaatio muuttaa ohjelmistokehitystä muuttamalla osaamistarpeita ja työnjakoa, datasta on tulossa yhä tärkeämpää ja pilvestä hallitseva alusta ohjelmistoille. Digitaalisen transformaation vuoksi tarvittavista taidoista tärkeimpiä ovat tekoäly, data-analytiikka, tietoturva, toimialaosaaminen, matalan koodin alustat, pilvialustat, kyberfyysiset järjestelmät ja esineiden internet. Digitaalinen transformaatio vaikuttaa ohjelmistokehitykseen muiden alojen digitaalisen transformaation toteuttamisen kautta, jolloin ohjelmistokehittäjiltä vaaditaan kykyä toteuttaa asiakkaiden haluamia ohjelmistoja, joka voi vaatia uusien teknologioiden osaamista. Se vaikuttaa myös ohjelmistoalaan ohjelmistokehityksen muuttuessa yhä enemmän automatisoiduksi. Tulevaisuudessa tekoälyn rooli ohjelmistokehityksessä kasvaa.

The importance of software architecture design decisions has been known for almost 20 years. Knowledge vaporisation is a problem in many projects, especially in the current fast-paced culture, where developers often switch from project to another. Documenting software architecture design decisions helps developers understand the software better and make informed decisions in the future. However, documenting architecture design decisions is highly undervalued. It does not create any revenue in itself, and it is often the disliked and therefore neglected part of the job. This literature review explores what methods, tools and practices are being suggested in the scientific literature, as well as, what practitioners are recommending within the grey literature. What makes these methods good or bad is also investigated. The review covers the past five years and 36 analysed papers. The evidence gathered shows that most of the scientific literature concentrates on developing tools to aid the documentation process. Twelve out of nineteen grey literature papers concentrate on Architecture Decision Records (ADR). ADRs are small template files, which as a collection describe the architecture of the entire system. The ADRs appear to be what practitioners have become used to using over the past decade, as they were first introduced in 2011. What is seen as beneficial in a method or tool is low-cost and low-effort, while producing concise, good quality content. What is seen as a drawback is high-cost, high-effort and producing too much or badly organised content. The suitability of a method or tool depends on the project itself and its requirements.

Parlamentaaristen aineistojen digitointi ja rakenteistaminen tutkimuskäyttöön on nouseva tutkimuksenala, jonka tiimoilta esimerkiksi Euroopassa on tällä hetkellä käynnissä useita kansallisia hankkeita. Tämä tutkielma on osa Semanttinen parlamentti -hanketta, jossa Suomen eduskunnan täysistuntojen puheenvuorot saatetaan ensimmäistä kertaa yhtenäiseksi, harmonisoiduksi aineistoksi koneluettavaan muotoon aina eduskunnan alusta vuodesta 1907 nykypäivään. Puheenvuorot ja niihin liittyvät runsaat kuvailutiedot on julkaistu kahtena versiona, parlamentaaristen aineistojen kuvaamiseen käytetyssä Parla-CLARIN XML -formaatissa sekä linkitetyn avoimen datan tietämysverkkona, joka kytkee aineiston osaksi laajempaa kansallista tietoinfrastruktuuria. Yhtenäinen puheenvuoroaineisto tarjoaa ennennäkemättömiä mahdollisuuksia tarkastella suomalaista parlamentarismia yli sadan vuoden ajalta monisyisesti ja automatisoidusti. Aineisto sisältää lähes miljoona erillistä puheenvuoroa ja linkittyy tiiviisti eduskunnan toimijoiden biografisiin tietoihin. Tässä tutkielmassa kuvataan puheenvuorojen esittämistä varten kehitetyt tietomallit ja puheenvuoroaineistojen keräys- ja muunnosprosessi sekä tarkastellaan prosessin ja syntyneen aineiston haasteita ja mahdollisuuksia. Toteutetun aineistojulkaisun hyödyllisyyden arvioimiseksi on Parla-CLARIN-muotoista aineistoa jo hyödynnetty poliittiseen kulttuuriin liittyvässä digitaalisten ihmistieteiden tutkimuksessa. Linkitetyn datan pohjalta on kehitetty semanttinen portaali, Parlamenttisampo, aineistojen julkaisemista ja tutkimista varten verkossa.

Ohjelmistotuotannon ammattilaiset tarvitsevat monenlaisia kompetensseja. Yksi näistä kompetensseista on kyky elinikäiseen oppimiseen, joka on tarpeellinen laajalla ja jatkuvasti muutoksessa olevalla alalla. ICT-aloille muodostuneen osaajatarpeen myötä elinikäisen oppimisen rooli onkin alkanut korostumaan entisestään. Tutkielman tarkoituksena on ollut lisätä ymmärrystä elinikäisen oppimisen roolista ohjelmistotuotannon ammattilaisen näkökulmasta. Tutkielmassa on pyritty tunnistamaan, mitä oppimisen muotoja hyödynnetään sekä millaisiin tarkoituksiin niitä käytetään, mitkä elinikäisen oppimisen kompetenssin osatekijät ovat tärkeitä sekä mitä haasteita elinikäiseen oppimiseen liittyy. Tutkimuksen aineisto on kerätty puolistrukturoiduilla haastatteluilla ohjelmistotuotannon ammattilaisten kanssa. Näiden haastattelujen tuloksia on verrattu tutkielmaa varten suoritetun kirjallisuuskatsauksen tuloksiin. Oppimisen muodoista informaalia oppimista hyödynnetään eniten ja erityisesti pienempiin oppimistarpeisiin. Nonformaalia ja formaalia oppimista taas hyödynnetään isompiin tarpeisiin, mutta harvemmin. Motivaatio, tiedonhaku ja metaoppiminen korostuvat keskeisinä elinikäisen oppimisen kompetenssin osatekijöinä. Ajanpuute ja itsensä motivoiminen mielletään yleisimmiksi haasteiksi elinikäistä oppimista koskien. Myös tiedonlähteisiin liittyvät puutteet sekä puutteellinen ymmärrys metaoppimisesta mielletään vaikeuttavan elinikäistä oppimista. Tutkielman havainnot tukevat elinikäisen oppimisen kompetenssin keskeistä roolia ohjelmistotuotannon ammattilaisilla. Kehitettävää löytyy kuitenkin vielä ohjelmistotuotannon ammattilaisten valmiuksista elinikäiseen oppimiseen, esimerkiksi metaoppimista koskien. Havainnot perustuvat kuitenkin lyhyemmän aikaa ohjelmistotuotannon ammattilaisina työskennelleiden kokemuksiin, joten lisää tutkimusta tarvitaan etenkin pitempään työskennelleiltä ohjelmistotuotannon ammattilaisilta.

The fast development cycles of Web User Interfaces (UI) create challenges for test automation to keep up with the changes in web elements. Test automation may suffer from test breakages after developers update the UIs of the System Under Test (SUT). Test breakages are not defects or bugs in the SUT, but a failure in test automation code. Failing to correctly locate web element from the UI, is one of the key reasons for test breakages to occur. Prior work to gain self-healing of element locators, has been traditionally done with different algorithms and recently with the help of Large Language Models (LLMs). This thesis aims to discover how to enable self-healing locators for Robot Framework Web UI tests, are there some web element locator types that are more easily repaired than others, and which LLMs should be used for this task. An experimental study was conducted for enabling self-healing locators for Robot Framework. Custom Robot Framework library was created with Python, which was tested for eight different locator strategies raised from locator breakage taxonomy. Results show that the best performance in self-healing locators is gained by using the bigger LLMs. GPT-4 Turbo and Mistral Large showed the best performance accuracy by repairing 87,5% of the locators in the Robot Framework test cases. The worst performer was Mistral 7B Instruct which was not able to correct any locators. Using LLMs for self-healing locators in Robot Framework tests is possible. To get the best results for self-healing locators, my results suggest that practitioners should focus on LLM prompt designing, in the usage of candidate algorithm with locator version history and use the biggest LLMs available if possible.