A systematic examination of knowledge loss in open source software projects

Context Open Source Software (OSS) development is a knowledge focused activity which relies heavily on contributors who can be volunteers or paid workers and are geographically distributed. While working on OSS projects contributors acquire project related individualistic knowledge and gain experience and skills, which often remains unshared with others and is usually lost once contributors leave a project. All software development organisations face the problem of knowledge loss as employees leave, but this situation is exasperated in OSS projects where most contributors are volunteers with largely unpredictable engagement durations. Contributor turnover is inevitable due to the transient nature of OSS project workforces causing knowledge loss, which threatens the overall sustainability of OSS projects and impacts negatively on software quality and contributor productivity. Objective The objective of this work is to deeply and systematically investigate the phenomenon of knowledge loss due to contributor turnover in OSS projects as presented in the state-of-the-art literature and to synthesise the information presented on the topic. Furthermore, based on the learning arising from our investigation it is our intention to identify mechanisms to reduce the overall effects of knowledge loss in OSS projects. Methodology We use the snowballing methodology to identify the relevant literature on knowledge loss due to contributor turnover in OSS projects. This robust methodology for a literature review includes research question, search strategy, inclusion, exclusion, quality criteria, and data synthesis. The search strategy, and inclusion, exclusions and quality criteria are applied as a part of snowballing procedure. Snowballing is considered an efficient and reliable way to conduct a systematic literature review, providing a robust alternative to mechanically searching individual databases for given topics. Result Knowledge sharing in OSS projects is abundant but there is no evidence of a formal strategy or practice to manage knowledge. Due to the dynamic and diverse nature of OSS projects, knowledge management is considered a challenging task and there is a need for a proactive mechanism to share knowledge in the OSS community for knowledge to be reused in the future by the OSS project contributors. From the collection of papers found using snowballing, we consolidated various themes on knowledge loss due to contributor turnover in OSS projects and identified 11 impacts due to knowledge loss in OSS projects, and 10 mitigations to manage with knowledge loss in OSS projects. Conclusion In this paper, we propose future research directions to investigate integration of proactive knowledge retention practices with the existing OSS practices to reduce the current knowledge loss problem. We suggest that there is insufficient attention paid to KM in general in OSS, in particular there would appear to an absence of proactive measures to reduce the potential impact of knowledge loss. We also propose the need for a KM evaluation metric in OSS projects, similar to the ones that evaluate health of online communities, which should help to inform potential consumers of the OSS of the KM status on a project, something that is not existent today

Аналіз проблеми застосування методів машинного навчання для оцінювання та прогнозування дефектів програмного забезпечення

An evaluation and analysis of literature sources, in which the methods of machine learning for predicting software defects, were investigated. The main characteristics of software defects are identified, such as complexity indicators, keywords, changes, software code size, and structural dependencies. The main methods and means of predicting software defects based on metrics by machine learning methods are characterized. A general scheme for predicting software defects is described, which makes it possible to conduct experiments and determine the presence or absence of a defect in a software module. The performance of a software defect prediction model strongly depends on the choice of data set, which is the first step in conducting research. Previous studies have been found to be mostly based on open-source datasets, and the software metrics used to build the models are mostly product metrics. The PROMISE data set is the most common in research, although the project data in the data set is outdated and dates back to 2004, 2005 and 2006. ​​While performing this work, modern scientific research in the field was analyzed. The classification methods used in the prediction of software defects have been revealed. It has been established that Logistic Regression, followed by Naive Bayes and Random Forest, are the most widely used classification methods in such models. An important stage for understanding the effectiveness of the model is its evaluation. Indicators of the evaluation of the effectiveness of the software defect forecasting model, which are most often used in research, have been revealed. It is found that f-measure, followed by recall and AUC, is the most common metric used to evaluate the performance of software defect prediction models. It has been found that in recent years there has been an increased interest in the use of software defect models and the classification of software defects based on code metrics and project characteristics. The relevance of evaluating and predicting software defects using machine learning methods is substantiated. Some aspects that require additional research have been identified. The directions of future research are determined, namely: feature selection methods, classifier selection methods, data preprocessing methods, construction of defect prediction models, development of software defect prediction methods and tools.Здійснено оцінювання та виконано аналіз літературних джерел, в яких досліджено методи машинного навчання для прогнозування дефектів програмного забезпечення. Визначено основні характеристики дефектів програмного забезпечення, такі як показники складності, ключові слова, зміни, розмір програмного коду та структурні залежності. Охарактеризовано основні методи та засоби прогнозування дефектів програмного забезпечення на основі метрик методами машинного навчання. Описано загальну схему прогнозування дефектів програмного забезпечення, яка дає змогу проводити експерименти та визначати наявність чи відсутність дефекту в програмному модулі. Продуктивність моделі передбачення дефектів програмного забезпечення істотно залежить від вибору набору даних, що є першим кроком проведення дослідження. Встановлено, що попередні дослідження здебільшого базуються на наборах даних з відкритим кодом, а програмні показники, які використовують для створення моделей, переважно є метриками продукту. Набір даних PROMISE (обіцянки) використовується в дослідженнях найчастіше, хоча дані проектів у наборі є застарілими та датуються 2004, 2005 та 2006 роками. Під час виконання цієї роботи проаналізовано сучасні наукові дослідження у галузі. Виявлено методи класифікації, що використовують під час прогнозування дефектів програмного забезпечення. Встановлено, що логістична регресія (англ. Logistic Regression), за якою слідує наївний Баєс (англ. Naive Bayes) та випадковий ліс (англ. Random Forest), є найбільш застосовуваними методами класифікації в таких моделях. Важливим етапом для розуміння ефективності моделі є її оцінювання. Виявлено показники оцінювання ефективності моделі прогнозування дефектів програмного забезпечення, що найчастіше використовують дослідженнях. З'ясовано, що f-measure, за якою слідує recall та AUC, є найпоширенішим показником, який використовується для оцінювання ефективності моделей передбачення дефектів програмного забезпечення. Виявлено, що за останні роки зріс інтерес до використання моделей дефектів програмного забезпечення та класифікації програмних дефектів на основі метрик коду та характеристик проекту. Обґрунтовано актуальність оцінювання та прогнозування дефектів програмного забезпечення методами машинного навчання. Встановлено деякі аспекти, які потребують додаткового дослідження. Визначено напрями майбутніх досліджень, а саме: методи вибору ознак, методи вибору класифікаторів, методи попереднього оброблення даних, побудова моделей прогнозування дефектів, розроблення методів і засобів прогнозування дефектів програмного забезпечення

Education data futures: critical, regulatory and practical reflections

The data collected from children at or through their participation in school are exponentially increasing in variety, velocity and volume. But whose interests are served by this ‘datafication’ of education and childhood? This essay collection offers critical, practical and creative reflections that identify exciting possibilities for beneficial uses of children’s education data as well as tackling the exploitative uses or misuse of such data. Collectively, the essays set out principled yet practical proposals for our children’s education data futures

An Investigation of the Effects of Writing Instruction in an Ungraded Informal Learning Environment

The purpose of this phenomenological study was to investigate and describe the teaching of writing in an informal setting through the voices of teachers and students. The intent of this study was to describe the roles of teachers and students in this learning environment and specifically to describe the role of writing response groups in this environment. Research in both writing instruction and informal education suggests that writing instruction is a good contextual match to camp-style informal education. Both informal education and writer\u27s workshop-style writing instruction put the individual and their choices at the center of the experience. VI This phenomenological study used the language of teacher and student participants gathered through the use of open-ended surveys, individual interviews, and focus group discussions in order to describe the experience of individuals at Young Writer\u27s Camp, and to look at these experiences collectively to answer the question, What really happens at Young Writer\u27s Camp and how does that happen? By contributing to a greater understanding of the interrelationship between this informal learning environment and the content of writing instruction, this study supports efforts to create more successful opportunities for writing instruction both inside and outside of the traditional English classroom. The results have implications for classroom teachers of writing, as well as schools and extracurricular programming agencies looking for information on how to effectively structure enrichment activities outside the context of the formal classroom

The Role of Interactive Web Broadcasts in Fostering Distance Learning Students’ Engagement with Practical Lab and Fieldwork

Practical work in science and technology disciplines is crucial for students’ understanding and mastery. For educators who teach those disciplines at a distance and for students who learn remotely, this endeavour may be challenging. The study presented in this thesis investigates the use of Interactive Web Broadcasts (IWBs) in five undergraduate practical science and technology modules at The Open University. The study examines the reasons for using IWBs, as well as the strategies and types of interactions that staff and students use to engage and interact with one another. The study gathered perspectives from academics (n=18); associate lecturers (n=10); technical production team (n=3); students (n=88), and an external guest expert about the purposes, strategies and motivations of participating in IWBs. The study used a qualitatively mixed-methods design. An adapted protocol of Flanders’s Interaction Analysis Categories was used to analyse the interaction patterns in the web broadcast transcripts and text-chat logs, and a discourse analysis coding scheme was applied to analyse the text-chat. Student online questionnaires were administered towards the end of the modules to capture the student perceptions of IWBs. Student interviews and staff focus groups were also conducted to gain a fuller picture of experiences of using and engaging with IWBs. Findings show that the purposes and aims of using IWBs are to facilitate student engagement, foster a sense of community, and demonstrate an authentic practice of the sciences in real-world contexts. The communicative strategies were primarily affective and met students’ interests and expectations. The IWBs mitigated feelings of isolation that are common in distance education environments. IWBs had positive impacts on professional teaching practices and fostered collegiality and collaboration among staff. The findings are relevant to other distance and traditional campus-based universities that teach practical science and technology, those who teach online using synchronous technology-mediated systems, and those who are interested in student engagement and practical work

Designing social media analytics tools to support non-market institutions: Four case studies using Twitter data

This research investigates the design of social media tools for non-market institutions, such as local government or community groups. At the core of this practice-based research is a software tool called LocalNets. LocalNets was developed to collect, analyse and visualise data from Twitter, thereby revealing information about community structure and community assets. It is anticipated that this information could help non- market institutions and the communities with which they work. Twitter users send messages to one another using the ‘@mention’ function. This activity is made visible publicly and has the potential to indicate a Twitter user’s participation in a ‘community structure’; that is, it can reveal an interpersonal network of social connections. Twitter activity also provides data about community assets (such as parks, shops and cinemas) when tweets mention these assets’ names. The context for this research is the Creative Exchange Hub (CX), one of four Knowledge Exchange Hubs for the Creative Economy funded by the UK Arts and Humanities Research Council (AHRC). Under the theme of ‘Digital Public Space’, the CX Hub facilitated creative research collaborations between PhD researchers, academics and non-academic institutions. Building on the CX model, this PhD research forged partnerships between local councils, non-public sector institutions that work with communities, software developers and academics with relevant subject expertise. Development of the LocalNets tool was undertaken as an integral part of the research. As the software was developed, it was deployed in relevant contexts through partnerships with a range of non-market institutions, predominantly located in the UK, to explore its use in those contexts. Four projects are presented as design case studies: 1) a prototyping phase, 2) a project with the Royal Society of Arts in the London Borough of Hounslow, 3) a multi-partner project in Peterborough, and 4) a project with Newspeak House, a technology and politics co-working space located in London. The case studies were undertaken using an Action Design Research method, as articulated by Sein et al. Findings from these case studies are grouped into two categories. The first are ‘Implementation findings’ which relate specifically to the use of data from Twitter. Second there are six ‘situated design principles’ which were developed across the case studies, and which are proposed as having potential application beyond Twitter data. The ‘Implementation findings’ include that Twitter can be effective for locating participants for focus groups on community topics, and that the opinions expressed directly in tweets are rarely sufficient for the local government of community groups to respond to. These findings could benefit designers working with Twitter data. The six situated design principles were developed through the case studies: two apply Burt’s brokerage social capital theory, describing how network structure relates to social capital; two apply Donath’s signalling theory – which suggests how social media behaviours can indicate perceptions of community assets; and two situated design principles apply Borgatti and Halgin’s network flow model – a theory which draws together brokerage social capital and signalling theory. The principles are applicable to social media analytics tools and are relevant to the goals of non-market institutions. They are situated in the context of the case studies; however, they are potentially applicable to social media platforms other than Twitter. Linders identifies a paucity of research into social media tools for non-market institutions. The findings of this research, developed by deploying and testing the LocalNets social media analytics tool with non-market institutions, aim to address that research gap and to inform practitioner designers working in this area

Implementando Lean UX em uma pequena equipe com rotatividade no contexto de um grupo de pesquisa universitário: relato de experiência

Os métodos de desenvolvimento que consideram a experiência do usuário (UX) são cada vez mais evidentes nas organizações. No entanto, existe um contexto desafiador em pequenas equipes com rotatividade de membros, como ocorre em grupos de pesquisa universitários, em que as equipes geralmente são formadas por pesquisadores, alunos de graduação e pós-graduação durante o período de seus cursos e bolsas. Nessa perspectiva, aplicou-se o Lean UX a uma pequena equipe de um grupo de pesquisa universitária. O estudo visou a levantar melhorias para os projetos mantidos pelo grupo, adotando uma padronização de métodos de desenvolvimento. Duas versões de uma interface gráfica foram desenvolvidas de acordo com os procedimentos Lean UX. Foram utilizadas técnicas de observação e questionários para analisar os resultados. Como resultados, foram observadas melhorias no tempo de execução dos experimentos, bem como aumento da satisfação do usuário com a interface, que foi considerada mais intuitiva, ágil e limpa. Em relação à equipe, houve relatos de maior satisfação e engajamento no processo de desenvolvimento

Covid-19 en Turquía: aburrimiento en el ocio, resiliencia psicológica, actividad física y estado emocional

This research was conducted in the quarantine process implemented in the CoViD-19 outbreak to examine the relationship between the perception of the boredom of individuals in leisure times and psychological resilience levels, and to describe the leisure activities of individuals, participation in physical activity and emotional state. Accordingly, a total of 2214 voluntary individuals living in Turkey participated with 909 men (41.1%) (age=33.83±10.73), and 1305 women (58.9%) (age=32,41±10.02). Within the scope of the study, information about demographic variables, physical activity and emotional state were collected with the form created by the researchers. In the study, "Leisure Boredom Scale" and "Psychological Resilience Scale" were used as the measurement tools. In the study, individuals were asked to write a word expressing their thoughts on the CoViD-19 process for descriptive analyses, and the collected data were visualized with the "MAXQDA" qualitative data analysis program. In addition, the data are presented in charts in the analysis of other variables. In the statistical analysis of the study, descriptive statistics were used, t-test was used to determine the difference between independent groups, Pearson correlation analysis was used to determine the relationships between variables, and simple linear regression analysis was used to determine the strength of the independent variable in predicting the dependent variable. As a result, predominantly negative emotional states were observed in individuals during the quarantine period. However, as the participation in physical activity increases, the level of psychological resilience will increase and the perception of boredom in leisure time will decrease. Besides, it was found that the perception of boredom in leisure time was an important determinant of the level of psychological resilience, and that it explains about 15% of the variance.Esta investigación se realizó en el proceso de cuarentena implementado en el brote de CoViD-19 para examinar la relación entre la percepción del aburrimiento de los individuos en los momentos de ocio y los niveles de resiliencia psicológica, y describir las actividades de ocio de los individuos, la participación en la actividad física y estado emocional. En consecuencia, participaron un total de 2214 personas voluntarias que vivían en Turquía, con 909 hombres (41,1%) (edad=33,83±10,73) y 1305 mujeres (58,9%) (edad=32,41±10,02).En el ámbito del estudio, se recogió información sobre variables demográficas, actividad física y estado emocional con el formulario creado por los investigadores. En el estudio, la "Escala de aburrimiento del ocio" y la "Escala de resiliencia psicológica" se utilizaron como herramientas de medición. En el estudio, se pidió a las personas que escribieran una palabra para expresar sus pensamientos sobre el proceso CoViD-19 para análisis descriptivos, y los datos recopilados se visualizaron con el programa de análisis de datos cualitativos "MAXQDA". Además, los datos se presentan en gráficos en el análisis de otras variables. En el análisis estadístico del estudio se utilizó estadística descriptiva, se utilizó la prueba t para determinar la diferencia entre grupos independientes, se utilizó el análisis de correlación de Pearson para determinar las relaciones entre las variables y se utilizó el análisis de regresión lineal simple para determinar la fuerza de la variable independiente en la predicción de la variable dependiente. Como resultado, se observaron estados emocionales predominantemente negativos en los individuos durante el período de cuarentena. Sin embargo, a medida que aumenta la participación en la actividad física, aumentará el nivel de resiliencia psicológica y disminuirá la percepción de aburrimiento en el tiempo libre. Además, se encontró que la percepción de aburrimiento en el tiempo libre fue un determinante importante del nivel de resiliencia psicológica, y que explica alrededor del 15% de la varianza.Universidad Pablo de Olavid