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Abstract: Diplomsko delo raziskuje in primerja zmogljivosti dveh ključnih programov za postprodukcijo videa, Adobe Premiere Pro in Adobe After Effects. Namen naloge je ugotoviti, kateri program je bolj primeren za določene naloge in kako ju optimalno uporabiti skupaj. S pomočjo teoretičnega pregleda in praktičnih primerov analiziramo funkcionalnosti, kot so barvna korektura, večkamerno urejanje, preoblikovanje časa, sledenje kameri in druge.
Keywords: postprodukcija, programska orodja za montažo, Adobe Premiere Pro, Adobe After Effects
Published in DKUM: 03.10.2024; Views: 0; Downloads: 10
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Abstract: V današnjem svetu se aplikacije hitro razvijajo, kar zahteva nenehne spremembe infrastrukture. V diplomskem delu je Terraform obravnavan kot orodje za infrastrukturo kot kodo (IaC), pri čemer so prikazane prednosti razvoja in uvajanja programske opreme. V nalogi smo primerjali priporočene prakse v orodju Terraform in poudarili njihov vpliv na široko obsežne projekte IaC. Analizirali smo obstoječo literaturo, da bi zasnovali eksperimentalno fazo in potrdili naše teoretične trditve s praktičnim projektom. Ugotovitve razkrivajo, da nekatere prakse sicer povečujejo učinkovitost projekta, vendar niso to edini odločilni dejavniki. Poleg tega se zavedamo, da raba IaC morda ni vedno ustrezna v vseh primerih.
Keywords: Terraform, IaC, Infrastruktura kot koda, DevOps, AWS
Published in DKUM: 19.09.2024; Views: 0; Downloads: 6
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Abstract: As artificial intelligence advances, source code completion assistants are becoming more advanced and powerful. Existing traditional assistants are no longer up to all the developers’ challenges. Traditional assistants usually present proposals in alphabetically sorted lists, which does not make a developer’s tasks any easier (i.e., they still have to search and filter an appropriate proposal manually). As a possible solution to the presented issue, intelligent assistants that can classify suggestions according to relevance in particular contexts have emerged. Artificial intelligence methods have proven to be successful in solving such problems. Advanced intelligent assistants not only take into account the context of a particular source code but also, more importantly, examine other available projects in detail to extract possible patterns related to particular source code intentions. This is how intelligent assistants try to provide developers with relevant suggestions. By conducting a systematic literature review, we examined the current intelligent assistant landscape. Based on our review, we tested four intelligent assistants and compared them according to their functionality. GitHub Copilot, which stood out, allows suggestions in the form of complete source code sections. One would expect that intelligent assistants, with their outstanding functionalities, would be one of the most popular helpers in a developer’s toolbox. However, through a survey we conducted among practitioners, the results, surprisingly, contradicted this idea. Although intelligent assistants promise high usability, our questionnaires indicate that usability improvements are still needed. However, our research data show that experienced developers value intelligent assistants highly, highlighting their significant utility for the experienced developers group when compared to less experienced individuals. The unexpectedly low net promoter score (NPS) for intelligent code assistants in our study was quite surprising, highlighting a stark contrast between the anticipated impact of these advanced tools and their actual reception among developers.
Keywords: intelligent assistants, source code completion, source code
Published in DKUM: 21.05.2024; Views: 188; Downloads: 15
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Abstract: Scaled agile development approaches are now used widely in modern software engineering, allowing businesses to improve teamwork, productivity, and product quality. The incorporation of artificial intelligence (AI) into scaled agile development methods (SADMs) has emerged as a potential strategy in response to the ongoing demand for simplified procedures and the increasing complexity of software projects. This paper explores the intersection of AI-driven assistants within the context of the scaled agile framework (SAFe) for large-scale software development, as it stands out as the most widely adopted framework. Our paper pursues three principal objectives: (1) an evaluation of the challenges and impediments encountered by organizations during the implementation of SADMs, (2) an assessment of the potential advantages stemming from the incorporation of AI in large-scale contexts, and (3) the compilation of aspects of SADMs that AI-driven assistants enhance. Through a comprehensive systematic literature review, we identified and described 18 distinct challenges that organizations confront. In the course of our research, we pinpointed seven benefits and five challenges associated with the implementation of AI in SADMs. These findings were systematically categorized based on their occurrence either within the development phase or the phases encompassing planning and control. Furthermore, we compiled a list of 15 different AI-driven assistants and tools, subjecting them to a more detailed examination, and employing them to address the challenges we uncovered during our research. One of the key takeaways from this paper is the exceptional versatility and effectiveness of AI-driven assistants, demonstrating their capability to tackle a broader spectrum of problems. In conclusion, this paper not only sheds light on the transformative potential of AI, but also provides invaluable insights for organizations aiming to enhance their agility and management capabilities.
Keywords: SAFe, scaled agile framework, AI, artificial intelligence, tools, assistants, agile, large-scale
Published in DKUM: 26.01.2024; Views: 342; Downloads: 51
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Abstract: Diplomsko delo je razdeljeno na dva dela, in sicer na teoretični in empirični del. V prvem delu na kratko opredelimo raziskovalni problem, zapišemo raziskovalna vprašanja in definiramo teoretične koncepte. Najprej ob zaključku študija študentje oddajo zaključno delo v referat v tiskani obliki. Ta dela obdelajo in shranijo v knjižnici. Z leti se je v visokošolskih fakultetnih knjižnicah pojavila prostorska stiska. Z razvojem informacijsko-komunikacijske tehnologije se po svetu in tudi pri nas pojavi ideja o digitalizaciji gradiva. Na Univerzi v Mariboru se pojavi repozitorij DKUM, kamor študentje oddajajo zaključna dela v elektronski obliki. Za potrebe diplomskega dela smo oblikovali anketni vprašalnik. Z njegovo pomočjo smo skušali raziskati in opisati, kako se je zaradi DKUM-a spremenil postopek oddaje zaključnih del na UM. Ugotavljamo, da so na Univerzi v Mariboru uspešno prešli s samo tiskane na elektronsko obliko oddaje zaključnih del. Skrajšali in poenotili so postopek oddaje zaključnih del. Omogočili so dostop do polnih besedil na daljavo. Digitalizacija je omogočila shranjevanje velikih količin podatkov na enem mestu in posledično odpravila problem prostorske stiske. Zaznali smo, da Izposoja tiskanih zaključnih del pada, medtem ko število vpogledov v polna besedila zaključnih del narašča.
Keywords: zaključna dela, digitalizacija, Digitalna knjižnica Univerze v Mariboru, Univerza v Mariboru
Published in DKUM: 05.10.2023; Views: 425; Downloads: 35
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