This thesis investigates the concept of citizen developers, in which employees in an organisation take on the role of developers and create software using low-code tools. The primary focus of this report is to examine the necessary steps an organisation must take when introducing citizen developers, particularly regarding solution maintenance. The purpose, therefore, is to establish a foundation for utilising the concept of citizen developers in the workplace, bridging the gap between users and developers. Furthermore, this study aims to explore the organisational structure required to incorporate citizen developers and identify the advantages and drawbacks associated with an organisational structure tailored for citizen developers. Citizen developers possess distinct roles and responsibilities within an organisation, making them experts in their respective fields. This expertise renders citizen developers a valuable resource, as they develop solutions to enhance their own work efficiency and effectiveness. A case company has been selected for this study, and interviews have been conducted with multiple employees from the organisation. Through analysis of these interviews and a comprehensive literature review, a governance model has been developed. The governance model delineates the level of responsibility citizen developers should assume, and an evaluation of the model also uncovers potential advantages and drawbacks of introducing citizen developers within an organisation. Ultimately, this thesis aims to contribute to research on the subject of citizen developers, with particular emphasis on solution maintenance.

Code comprehension is crucial for software development, as it ensures that code is easy to read andunderstand.Identifiers, such as variable-, function-, and methodnames playa significant role in code comprehension, and it isimportantthat they are written using meaningful names so thateveryone understands their usage. However, sometimes theycontainabbreviations that can have significantly differentexplanationsdepending on the context they are written in, which couldnegatively impact comprehension if the context is notfully known.Large code bases can be hard to navigate but with thehelp ofCode Visualization Tool (CVT) they can provide anoverviewthat helpfully displays the program’s flow and call graphs.Tohelp with abbreviations in large code bases this studywasconducted to investigate the use of Abbreviation Resolver(AR)techniques integrated into a CVT called Doxygen to seethe effecton identifier comprehension. A survey was performed ontwoopen-source projects with questions about thecomprehensionof different methods using their identifiers with bothresolvedidentifiers and abbreviated identifiers. The survey utilizedAR toanalyze participant ́s understanding of abbreviations todetermine it ́s usefulness. The findings suggest that the use ofa CVTwith an integrated AR helps in understanding uncommonanddomain-knowledge abbreviations which contribute toimprovedcode comprehension.

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Code clones are pieces of code that have the same functionality. While some clones may structurally match one another, others may look drastically different. The inclusion of code clones clutters a code base, leading to increased costs through maintenance. Duplicate code is introduced through a variety of means, such as copy-pasting, code generated by tools, or developers unintentionally writing similar pieces of code. While manual clone identification may be more accurate than automated detection, it is infeasible due to the extensive size of many code bases. Software code clone detection methods have differing degree of success based on the analysis performed. This thesis outlines a method of detecting clones using a program dependence graph and subgraph isomorphism to identify similar subgraphs, ultimately illuminating clones. The project imposes few constraints when comparing code segments to potentially reveal more clones.

Significant research has been done regarding the influence of age of acquisition (i.e., the age at which one is exposed to a second language (L2)) on L2 learning (e.g., Johnson & Newport, 1989; Bialystock & Hakuta, 1999). Some researchers have shown that bilinguals who have learned their second language early in life may differ in their fluency from bilinguals who learned their second language later in life (White & Genesee, 1996; Flege, 1999). Specifically, studies have suggested that bilinguals who have not acquired their L2 by puberty will never acquire native-like proficiency (Lenneberg, 1967); however, others claim that there is not one particular age after which native-like language proficiency cannot be achieved (Birdsong and Molis, 1998; Flege; 1999). However, little research has been done regarding the effect that age of acquisition has on how bilinguals code switch and what rules govern this code-switching. Early research by Poplack (1980) found that late (i.e., those who learned the L2 in adulthood), less fluent bilinguals had different code switching tendencies than early (i.e., those who learned L2 in childhood), more fluent bilinguals. Lipski (1985) suggested that early bilinguals will engage in intrasentential switching while late bilinguals will rarely do so. In the present study, 26 early and late Spanish-English bilingual speakers made acceptability judgments on intra- and intersentential switches. Results indicate that there is no statistical difference between early and late bilinguals when responding to whether a mix was good or bad, and how good or bad a mix was. There were, however, trends in the results which indicate that early bilinguals may respond faster to code switches than late bilinguals, suggesting that early and late bilinguals may process language differently. Further research is needed to confirm this finding.

Latter-day Saints are recognized throughout the world by their peculiar dietary code known as the Word of Wisdom. It is accepted by the Church as revelation and contains both proscriptions and prescriptions. Different levels of interpretation, understanding and observance of the principles in the Word of Wisdom make it a unique Latter-day Saint religious health code. The study population consists of college-aged male and female members of The Church of Jesus Christ of Latter-day Saints from Brigham Young University and a Latter-day Saint student stake affiliated with Utah Valley University. A questionnaire was conceptualized, designed and validated over a one year rigorous process. The responses of 720 participants were evaluated using frequency distributions and measures of central tendency. The t statistic was used for testing the null hypothesis of equality of means between independent groups and computed using approximate degrees of freedom from Satterhwaite's approximation when the variances between the two groups are unequal. Logistic regression was used with the stepwise option to identify demographic variables associated with selected interpretation variables. Two-sided tests of significance were based on the 0.05 level against a null hypothesis of no association. Participants generally interpret and comply with the proscriptions in the Word of Wisdom. Participants can identify the prescriptions, but actual behaviors are far below scientific and government recommended minimums. The interpretations and behaviors of this sample provide valuable insights for those interested in the Word of Wisdom and its connection to the standards of the Church.

Developments in automatic code generation are evolving remarkably fast, with companies and researchers competing to reach human-level accuracy and capability. Advancements in this field primarily focus on using machine learning models for end-to-end code generation. This project introduces the system CodeFromVoice, which explores an alternative method for code generation. This method relies on existing Natural Language Processing models combined with traditional parsing methods. CodeFromVoice shows that this approach can generate code from text or transcribed speech using Automatic Speech Recognition. The generated code is limited in complexity and restricted to the context of an existing application but achieves a Word Error Rate of less than 25%. / Utvecklingen av automatisk kodgenerering visar stora framsteg, med företag och forskare som tävlar om att nå mänsklig nivå av noggrannhet och förmåga. Framsteg inom detta område fokuserar främst på användning av maskininlärningsmodeller för hela kodgenerering processen. Detta projekt introducerar systemet CodeFromVoice, som utforskar en alternativ metod för kodgenerering. Denna metod bygger på befintliga NLP-modeller kombinerat med traditionella parsning metoder. CodeFromVoice visar att detta tillvägagångssätt kan generera kod från text eller transkriberat tal med automatisk taligenkänning. Den genererade koden är begränsad i komplexitet och begränsad till sammanhanget av en existerande applikation, men uppnår en ordfelfrekvens som är mindre än 25%.

Background. Machine learning (ML) has rapidly grown in popularity, becoming a vital part of many industries. This swift expansion has brought about new challenges to technical debt, maintainability and the general software quality of ML systems. With ML applications becoming more prevalent, there is an emerging need for extensive research to keep up with the pace of developments. Currently, the research on code smells in ML applications is limited and there is a lack of tools and studies that address these issues in-depth. This gap in the research highlights the necessity for a focused investigation into the validity of ML-specific code smells in ML applications, setting the stage for this research study. Objectives. Addressing the limited research on ML-specific code smells within Python-based ML applications. To achieve this, the study begins with the identification of these ML-specific code smells. Once recognized, the next objective is to choose suitable methods and tools to design and develop a static code analysis tool based on code smell criteria. After development, an empirical evaluation will assess both the tool’s efficacy and performance. Additionally, feedback from industry professionals will be sought to measure the tool’s feasibility and usefulness. Methods. This research employed Design Science Methodology. In the problem identification phase, a literature review was conducted to identify ML-specific code smells. In solution design, a secondary literature review and consultations with experts were performed to select methods and tools for implementing the tool. Additionally, 160 open-source ML applications were sourced from GitHub. The tool was empirically tested against these applications, with a focus on assessing its performance and efficacy. Furthermore, using the static validation method, feedback on the tool’s usefulness was gathered through an expert survey, which involved 15 ML professionals from Ericsson. Results. The study introduced MLpylint, a tool designed to identify 20 ML-specific code smells in Python-based ML applications. MLpylint effectively analyzed 160ML applications within 36 minutes, identifying in total 5380 code smells, although, highlighting the need for further refinements to each code smell checker to accurately identify specific patterns. In the expert survey, 15 ML professionals from Ericsson acknowledged the tool’s usefulness, user-friendliness and efficiency. However, they also indicated room for improvement in fine-tuning the tool to avoid ambiguous smells. Conclusions. Current studies on ML-specific code smells are limited, with few tools addressing them. The development and evaluation of MLpylint is a significant advancement in the ML software quality domain, enhancing reliability and reducing associated technical debt in ML applications. As the industry integrates such tools, it’s vital they evolve to detect code smells from new ML libraries. Aiding developers in upholding superior software quality but also promoting further research in the ML software quality domain.

Due to the surge in the development of software, people in the software industry have a need for higher coding quality in different programming languages. A “code with good quality” can be defined as code that is written in a way that follows the rules or, in other words, conventions for, i.e., comments, proper indentation, clear notations, simplicity, naming, etc. There are coding style guidelines extracted from Java and Oracle code conventions to have readable, maintainable source code; however, the current studies do not answer the question of to what extent the open-source systems follow these guidelines. Finding the violations of conventions at the early stages of software development is essential because the changes are costly and impossible in the later stages. As a result, adhering to coding conventions will facilitate code readability and maintainability. Thus, this study intends to analyze the results from several code quality tools, make a comparison among them and, based on the outcomes, develop a new tool that covers the probable missing conventions in the studied code-checking tools.

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