Browsing by Author "Ogenrwot, Daniel"

Now showing 1 - 4 of 4
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    Comparison of Occurrence of Design Smells in Desktop and Mobile Applications
    (ACSE, 2020) Ogenrwot, Daniel; Nakatumba-Nabende, Joyce; Chaudron, Michel R.V.
    Design smells are symptoms of poor solutions to recurring design problems in a software system. Those symptoms have a direct negative impact on software quality by making it difficult to comprehend and maintain. In this paper we compare the occurrence of design smells between different technological ecosystems: windows/desktop and android/mobile. This knowledge is significant for various software maintenance activities such as program quality assurance and refactoring. To supplement previous findings, our study aimed at (a) understanding if and how the relationship among design smells differs across windows and mobile applications and (b) determining the groups of design smells that tend to occur frequently together and the magnitude of their occurrence in windows and mobile applications. In this study, we explored the use of statistics and unsupervised learning on a dataset consisting of twelve (12) Javabased open-source projects mined from GitHub. We identified fifteen (15) most frequent design smells across desktop and mobile applications. Additionally, a clustering technique revealed which groups of design smells that often co-occur. Specifically, {SpeculativeGenerality, SwissArmyKnife} and {LongParameterList, ClassDataShouldBePrivate} are observed to occur frequently together in desktop and mobile applications.
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    From Undergraduate (Software) Capstone Projects to Start-ups: Challenges and Opportunities in Higher Institutions of Learning
    (Middle East Conference on Software Engineering, 2022) Ogenrwot, Daniel; Olok Tabo, Geoffrey; Aber, Kevin; Nakatumba-Nabende, Joyce
    The capstone project is a fundamental part of almost all science and engineering degrees. It is not only a requirement for the partial fulfillment of an accredited university programme but also a method of assessing the students’ general mastery of concepts, critical thinking, problem-solving, and transferable skills. Annually, final-year undergraduate students offering computing programmes in Uganda build innovative software solutions to real-world problems within and outside their community. Anecdotal evidence indicates that most of those innovations have the potential for commercialization and transformation into technology-based businesses. However, limited progress has been made to commercialize students’ projects, and promising solutions are “buried” within academic reports. To this end, our research aims to explain the challenges and opportunities in the commercialization of students’ capstone projects across two (2) undergraduate computing programmes (Bachelor of Science in Computer Science and Bachelor of Information Technology) offered at Gulu University in Uganda. Using exploratory research design, we reviewed eighty-six (86) capstone projects, curricula, and a facilitated students & stakeholders’ workshop report. This paper articulates factors hindering the commercialization of undergraduate software capstone projects and recommends mitigating measures. It also proposes a framework for extending capstone course design from a traditional curriculum structure to an inclusive industry and community-oriented approach capable of turning ideas into business start-ups. The findings from this research are expected to inform higher institutions of learning in Africa in developing novel pedagogical approaches for orchestrating (software) capstone project courses that are inclusive and profitable beyond the academic setting.
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    Integration of design smells and role-stereotypes classification dataset
    (Data in Brief, 2021) Ogenrwot, Daniel; Nakatumba-Nabende, Joyce; Chaudron, Michel R.V.
    Design smells are recurring patterns of poorly designed (fragments of) software systems that may hinder main- tainability. Role-stereotypes indicate generic responsibilities that classes play in system design. Although the concepts of role-stereotypes and design smells are widely divergent, both are significant contributors to the design and mainte- nance of software systems. To improve software design and maintainability, there is a need to understand the relation- ship between design smells and role stereotypes. This pa- per presents a fine-grained dataset of systematically inte- grated design smells detection and role-stereotypes classi- fication data. The dataset was created from a collection of twelve (12) real-life open-source Java projects mined from GitHub. The dataset consists of 18 design smells columns and 2,513 Java classes (rows) classified into six (6) role- stereotypes taxonomy. We also clustered the dataset into ten (10) different clusters using an unsupervised learning algo- rithm. Those clusters are useful for understanding the groups of design smells that often co-occur in a particular role- stereotype category. The dataset is significant for understand- ing the non-innate relationship between design smells and role-stereotypes.
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    Seeing the air in detail: Hyperlocal air quality dataset collected from spatially distributed AirQo network
    (Data in Brief, 2022) Sserunjogi, Richard; Ssematimba, Joel; Okure, Deo; Ogenrwot, Daniel; Adong, Priscilla; Muyama, Lillian; Nsimbe, Noah; Bbaale, Martin; Bainomugisha, Engineer
    Air pollution is a major global challenge associated with an increasing number of morbidity and mortality from lung can- cer, cardiovascular and respiratory diseases, among others. However, there is scarcity of ground monitoring air quality data from Sub-Saharan Africa that can be used to quantify the level of pollution. This has resulted in limited targeted air pollution research and interventions e.g. health impacts, key drivers and sources, economic impacts, among others; ultimately hindering the establishment of effective manage- ment strategies. This paper presents a dataset of air quality observations collected from 68 spatially distributed monitor- ing stations across Uganda. The dataset includes hourly PM 2 . 5 and PM 10 data collected from low-cost air quality monitoring devices and one reference grade monitoring device over a pe- riod ranging from 2019 to 2020. This dataset contributes to- wards filling some of the data gaps witnessed over the years in ground level monitored ambient air quality in Sub-Saharan Africa and it can be useful to various policy makers and re- searchers.