Using Bayesian networks to estimate strategic indicators in the context of rapid software development

Background: During Rapid Software Development, a large amount of project and development data can be collected from different and heterogeneous data sources. Aims: Design a methodology to process these data and turn it into relevant strategic indicators to help companies make meaningful decisions. Method: We adapt an existing methodology to create and estimate strategic indicators using Bayesian Networks in the context of Rapid Software Development, and applied it to a use case. Results: Applying the methodology in the use case, we create a model to predict product quality based on software factors and metrics, using companies’ business knowledge and collected data. Conclusions: We proved the methodology’s feasibility and obtained positive feedback from the company’s use case.Postprint (author's final draft

An Additional Set of (Automated) Eyes: Chatbots for Agile Retrospectives

Recent advances in natural-language processing and data analysis allow software bots to become virtual team members, providing an additional set of automated eyes and additional perspectives for informing and supporting teamwork. In this paper, we propose employing chatbots in the domain of software development with a focus on supporting analyses and measurements of teams' project data. The software project artifacts produced by agile teams during regular development activities, e.g. commits in a version control system, represent detailed information on how a team works and collaborates. Analyses of this data are especially relevant for agile retrospective meetings, where adaptations and improvements to the executed development process are discussed. Development teams can use these measurements to track the progress of identified improvement actions over development iterations. Chatbots provide a convenient user interface for interacting with the outcomes of retrospectives and the associated measurements in a chat-based channel that is already being employed by team members.Comment: Accepted at the 1st International Workshop on Bots in Software Engineering (May 28th, 2019, Montreal, Canada), collocated with ICSE 2019 (https://botse.github.io/

A Framework for Leveraging Artificial Intelligence in Project Management

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis dissertation aims to support the project manager in their daily tasks. As we use artificial intelligence (AI) and machine learning (ML) in everyday life, it is necessary to include them in business and change traditional ways of working. For the purpose of this study, it is essential to understand challenges and areas of project management and how artificial intelligence can contribute to them. A theoretical overview, applying the knowledge of project management, will show a holistic view of the current situation in the enterprises. The research is about artificial intelligence applications in project management, the common activities in project management, the biggest challenges, and how AI and ML can support it. Understanding project managers help create a framework that will contribute to optimizing their tasks. After designing and developing the framework for applying artificial intelligence to project management, the project managers were asked to evaluate. This study is essential to increase awareness among the stakeholders and enterprises on how automation of the processes can be improved and how AI and ML can decrease the possibility of risk and cost along with improving the happiness and efficiency of the employees

Measures related to social and human factors that influence productivity in software development teams

Software companies need to measure their productivity. Measures are useful indicators to evaluate processes, projects, products, and people who are part of software development teams. The results of these measurements are used to make decisions, manage projects, and improve software development and project management processes. This research is based on selecting a set of measures related to social and human factors (SHF) that influence productivity in software development teams and therefore in project management. This research was performed in three steps. In the first step, there was performed a tertiary literature review aimed to identify measures related to productivity. Then, the identified measures were submitted for its evaluation to project management experts and finally, the measures selected by the experts were mapped to the SHF. A set of 13 measures was identified and defined as a key input for designing improvement strategies. The measures have been compared to SHF to evaluate the development team\u27s performance from a more human context and to establish indicators in productivity improvement strategies of software projects. Although the number of productivity measures related to SHF is limited, it was possible to identify the measures used in both traditional and agile contexts

Gender Differences in Personality Traits of Software Engineers

There is a growing body of gender studies in software engineering to understand diversity and inclusion issues, as diversity is recognized to be a key issue to healthy teams and communities. A second factor often linked to team performance is personality, which has received far more attention. Very few studies, however, have focused on the intersection of these two fields. Hence, we set out to study gender differences in personality traits of software engineers. Through a survey study we collected personality data, using the HEXACO model, of 483 software engineers. The data were analyzed using a Bayesian independent sample t-test and network analysis. The results suggest that women score significantly higher in Openness to Experience, Honesty-Humility, and Emotionality than men. Further, men show higher psychopathic traits than women. Based on these findings, we develop a number of propositions that can guide future research

Um processo baseado em redes bayesianas para avaliação da aplicação do scrum em projetos de software.

O aumento na utilização de métodos ágeis tem sido motivado pela necessidade de respostas rápidas a demandas de um mercado volátil na área de software. Em contraste com os tradicionais processos dirigidos a planos, métodos ágeis são focados nas pessoas, orientados à comunicação, flexíveis, rápidos, leves, responsivos e dirigidos à aprendizagem e melhoria contínua. Como consequência, fatores subjetivos tais como colaboração, comunicação e auto-organização são chaves para avaliar a maturidade do desenvolvimento de software ágil. O Scrum, focado no gerenciamento de projetos, é o método ágil mais popular. Ao ser adotado por uma equipe, a aplicação do Scrum deve ser melhorada continuamente sendo complementado com práticas e processos de desenvolvimento e gerenciamento ágeis. Apesar da Reunião de Retrospectiva, evento do Scrum, ser um período reservado ao final de cada sprint para a equipe refletir sobre a melhoria do método de desenvolvimento, não há procedimentos claros e específicos para a realização da mesma. Na literatura, há diversas propostas de soluções, embora nenhuma consolidada, para tal. Desta forma, o problema em questão é: como instrumentar o Scrum para auxiliar na melhoria contínua do método de desenvolvimento com foco na avaliação do processo de engenharia de requisitos, equipe de desenvolvimento e incrementos do produto? Nesta tese, propõe-se um processo sistemático baseado em redes bayesianas para auxiliar na avaliação da aplicação do Scrum em projetos de software, instrumentando o método para auxiliar na sua melhoria contínua com foco na avaliação do processo de engenharia de requisitos, equipe de desenvolvimento e incrementos do produto. A rede bayesiana foi construída por meio de um processo de Engenharia de Conhecimento de Redes Bayesianas. Uma base de dados, elicitada de dezoito projetos reais de uma empresa, foi coletada por meio de um questionário. Essa base de dados foi utilizada para avaliar a acurácia da predição da Rede Bayesiana. Como resultado, a previsão foi correta para quatorze projetos (acurácia de 78%). Dessa forma, conclui-se que o modelo é capaz de realizar previsões com acurácia satisfatória e, dessa forma, é útil para auxiliar nas tomadas de decisões de projetos Scrum.The use of Agile Software Development (ASD) is increasing to satisfy the need to respond to fast moving market demand and gain market share. In contrast with traditional plan-driven processes, ASD are people and communication-oriented, flexible, fast, lightweight, responsive, driven for learning and continuous improvement. As consequence, subjective factors such as collaboration, communication and self-management are key to evaluate the maturity of agile adoption. Scrum, which is focused on project management, is the most popular agile method. Whenever adopted, the usage of Scrum must be continuously improved by complementing it with development and management practices and processes. Even though the Retrospective Meeting, a Scrum event, is a period at the end of each sprint for the team to assess the development method, there are no clear and specific procedures to conduct it. In literature, there are several, but no consolidated, proposed solutions to assist on ASD adoption and assessment. Therefore, the research problem is: how to instrument Scrum to assist on the continuous improvement of the development method focusing on the requirements engineering process, development team and product increment? In this thesis, we propose a Bayesian networks-based process to assist on the assessment of Scrum-based projects, instrumenting the software development method to assist on its continuous improvement focusing on the requirements engineering process, development team and product increments. We have built the Bayesian network using a Knowledge Engineering Bayesian Network (KEBN) process that calculates the customer satisfaction given factors of the software development method. To evaluate its prediction accuracy, we have collected data from 18 industry projects from one organization through a questionnaire. As a result, the prediction was correct for fourteen projects (78% accuracy). Therefore, we conclude that the model is capable of accurately predicting the customer satisfaction and is useful to assist on decision-support on Scrum projects

Self-Organizing Teams in Online Work Settings

As the volume and complexity of distributed online work increases, the collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of such teams by grouping workers according to a set of predefined decision criteria. This approach micro-manages workers, who have no say in the team formation process. Depriving users of control over who they will work with stifles creativity, causes psychological discomfort and results in less-than-optimal collaboration results. In this work, we propose an alternative model, called Self-Organizing Teams (SOTs), which relies on the crowd of online workers itself to organize into effective teams. Supported but not guided by an algorithm, SOTs are a new human-centered computational structure, which enables participants to control, correct and guide the output of their collaboration as a collective. Experimental results, comparing SOTs to two benchmarks that do not offer user agency over the collaboration, reveal that participants in the SOTs condition produce results of higher quality and report higher teamwork satisfaction. We also find that, similarly to machine learning-based self-organization, human SOTs exhibit emergent collective properties, including the presence of an objective function and the tendency to form more distinct clusters of compatible teammates

Identification of Critical Factors and Their Interrelationships to Design Agile Supply Chain : Special Focus to Oil and Gas Industries

Purpose – This research attempted to identify the most critical factors and their inter-relationships to ensure designing agile supply chain, especially in oil and gas industry. This factors identification process is performed through developing a conceptual framework and the use of Interpretive Structural Modelling (ISM) tool. Design/methodology/approach – This study is conducted through an extensive literature review and questionnaires survey to identify and refine the critical factors that ensure the agile supply chain in oil and gas industry. In addition, several brainstorming sessions with the experts in the field of oil and gas industries were organized with the objective to interpret the contextual inter-relationships between the identified factors. The outcomes from the literature reviews, interview questions and experts’ opinions were used to develop a diagraph and MICMAC analysis to know the drivers of agility in supply chain. Findings –From this study, 34 enablers and 12 factors were identified, which are responsible to ensure agile supply chain in oil and gas industry. Out of these identified factors, top management commitment, strategic alignment, competency of management and integration of information and systems technology are found to be the critical drivers of supply chain agility. On the other hand, government regulations, transportation and logistics flexibility and production planning and control falls under the category of dependent factors. Originality/value – The identified factors and their interrelationships can be a valuable aid to ensure and measure the agility in supply chain, especially in oil and gas industry. These identified factors and their defined consequences will help managers and concerned authorities in oil and gas industry to take better decision to improve the agility level of their supply chain.©2020 Springer Nature. This is a post-peer-review, pre-copyedit version of an article published in Global Journal of Flexible Systems Management. The final authenticated version is available online at: http://dx.doi.org/10.1007/s40171-020-00247-5fi=vertaisarvioitu|en=peerReviewed

Do You Know What I Know?:Situational Awareness of Co-located Teams in Multidisplay Environments

Modern collaborative environments often provide an overwhelming amount of visual information on multiple displays. In complex project settings, the amount of visual information on multiple displays, and the multitude of personal and shared interaction devices in these environments can reduce the awareness of team members on ongoing activities, the understanding of shared visualisations, and the awareness of who is in control of shared artifacts. Research reported in this thesis addresses the situational awareness (SA) support of co-located teams working on team projects in multidisplay environments. Situational awareness becomes even more critical when the content of multiple displays changes rapidly, and when these provide large amounts of information. This work aims at getting insights into design and evaluation of shared display visualisations that afford situational awareness and group decision making. This thesis reports the results of three empirical user studies in three different domains: life science experimentation, decision making in brainstorming teams, and agile software development. The first and the second user studies evaluate the impact of the Highlighting-on-Demand and the Chain-of-Thoughts SA on the group decision-making and awareness. The third user study presents the design and evaluation of a shared awareness display for software teams. Providing supportive visualisations on a shared large display, we aimed at reducing the distraction from the primary task, enhancing the group decision-making process and the perceived task performance