Leveraging the Power of Crowds: Automated Test Report Processing for The Maintenance of Mobile Applications

Crowdsourcing is an emerging distributed problem-solving model combining human and machine computation. It collects intelligence and knowledge from a large and diverse workforce to complete complex tasks. In the software engineering domain, crowdsourced techniques have been adopted to facilitate various tasks, such as design, testing, debugging, development, and so on. Specifically, in crowdsourced testing, crowdsourced workers are given testing tasks to perform and submit their feedback in the form of test reports. One of the key advantages of crowdsourced testing is that it is capable of providing engineers software engineers with domain knowledge and feedback from a large number of real users. Based on diverse software and hardware settings of these users, engineers can bugs that are not caught by traditional quality assurance techniques. Such benefits are particularly ideal for mobile application testing, which needs rapid development-and-deployment iterations and support diverse execution environments. However, crowdsourced testing naturally generates an overwhelming number of crowdsourced test reports, and inspecting such a large number of reports becomes a time-consuming yet inevitable task. This dissertation presents a series of techniques, tools and experiments to assist in crowdsourced report processing. These techniques are designed for improving this task in multiple aspects: 1. prioritizing crowdsourced report to assist engineers in finding as many unique bugs as possible, and as quickly as possible; 2. grouping crowdsourced report to assist engineers in identifying the representative ones in a short time; 3. summarizing the duplicate reports to provide engineers with a concise and accurate understanding of a group of reports; In the first step, I present a text-analysis-based technique to prioritize test reports for manual inspection. This technique leverages two key strategies: (1) a diversity strategy to help developers inspect a wide variety of test reports and to avoid duplicates and wasted effort on falsely classified faulty behavior, and (2) a risk-assessment strategy to help developers identify test reports that may be more likely to be fault-revealing based on past observations.Together, these two strategies form our technique to prioritize test reports in crowdsourced testing. Moreover, in the mobile testing domain, test reports often consist of more screenshots and shorter descriptive text, and thus text-analysis-based techniques may be ineffective or inapplicable. The shortage and ambiguity of natural-language text information and the well-defined screenshots of activity views within mobile applications motivate me to propose a novel technique based on using image understanding for multi-objective test-report prioritization. This technique employs the Spatial Pyramid Matching (SPM) technique to measure the similarity of the screenshots, and apply the natural-language processing technique to measure the distance between the text of test reports. Next, I design and implement CTRAS: a novel approach to leveraging duplicates to enrich the content of bug descriptions and improve the efficiency of inspecting these reports. CTRAS is capable of automatically aggregating duplicates based on both textual information and screenshots, and further summarizes the duplicate test reports into a comprehensive and comprehensible report.I validate all of these techniques on industrial data by collaborating with several companies. The results show my techniques can improve both the efficiency and effectiveness of crowdsourced test report processing. Also, I suggest settings for different usage scenarios and discuss future research directions

Quality of experience-centric management of adaptive video streaming services : status and challenges

Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming ( HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users' QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users' QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users' QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server-and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years

Towards Clasification Exploration in Spatial Crowdsourcing Domain: A Systematic Literature Review

Today, spatial crowdsourcing concept has been widely applied in various fields. The increasing ofmobile user and adoption of social network has catalyst spatial crowdsourcing growth. It has madevarious types of data to be easily collected and transmitted from different geographical location.However, the massive amounts of task in spatial area bring challenges for the online system tomanage especially when the task is heterogeneous, and the interactions are dynamic. Such scenario has alerted the researchers to understand different types of information in order to make taskassignment reliable and efficient.This study investigates current state of task assignment for spatialcrowdsourcing. It basically, aims to identify several issues like trend in publication and crowdcomputing areas that studies task assignment in crowdsourcing. We used Systematic LiteratureReview (SLR) method for analysing the trends and significance of task classification for betterdynamic crowd-computing

Natural language processing methods for document-based requirements specification and validation tasks

Tesi amb menció de Doctorat InternacionalTesi en modalitat de compendi de publicacionsIn reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Universitat Politècnica de Catalunya's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink(English) Requirements engineering (RE) is fundamental to successful software development, especially in modern, large-scale projects. Efficient management of text-based artefacts is key to accurate elicitation, refinement, and validation of requirements. Despite the industrial trend towards adopting natural language processing (NLP) methods, challenges in their pervasiveness, reliability, scalability, and reusability persist. Moreover, the advent of large language models (LLMs) has set the groundwork for further research in automated document analysis in the field of RE. This thesis explores the integration of NLP methods and tools to automate and enhance RE tasks (NLP4RE) in three document-oriented areas: requirements traceability, requirements analysis for information retrieval, and requirements feedback gathering. For requirements traceability, methods for dependency and duplicate detection in text-based requirements documents are proposed and evaluated. In requirements analysis, a knowledge graph-based approach is developed to create adaptive, crowdsourced repositories of RE-related documents. For requirements feedback gathering, techniques for extracting features from user reviews and analyzing feedback are presented and evaluated. This research is shaped in the context of multiple case and sample studies validated empirically, demonstrating their effectiveness in real-world scenarios. The contributions presented in this thesis entail advancements in streamlining RE tasks and improving the accuracy, efficiency and adoption of NLP4RE tools and methods. Ultimately, this thesis aims to provide novel insights, methodologies and technical contributions to the NLP4RE field.(Català) L'enginyeria de requisits (RE) és fonamental per a l'èxit del desenvolupament de programari, especialment en projectes moderns i de gran escala. La gestió eficient dels documents de text generats durant aquesta fase és clau per a la correcta obtenció, refinament i validació dels requisits. Tot i la tendència industrial cap a l'adopció de mètodes de processament del llenguatge natural (NLP), encara persisteixen diversos reptes relacionats amb la seva presència, fiabilitat, escalabilitat i reutilització. A més, l'aparició de grans models de llenguatge (LLMs) ha establert les bases per a una recerca dedicada a l'anàlisi automàtica de documents en el camp de la RE. Aquesta tesi explora la integració de mètodes i eines de NLP per automatitzar i millorar les tasques de RE (NLP4RE) en tres àrees orientades a documents: traçabilitat de requisits, anàlisi i recuperació d'informació de requisits, i recollida de retroalimentació sobre els requisits. Per a la traçabilitat de requisits, es proposen i s'avaluen mètodes per a la detecció de dependències i duplicats en documents de requisits basats en text. En l'anàlisi de requisits, es proposa un desenvolupament basat en grafs de coneixement per crear repositoris adaptables de documents de proveïment participatiu relacionats amb la RE. Per a la recollida de retroalimentació, es presenten tècniques per extreure característiques i funcionalitats dels comentaris dels usuaris i analitzar-los. Aquesta investigació es basa en múltiples casos d'estudi validats empíricament, demostrant la seva efectivitat en escenaris del món real. Les contribucions presentades en aquesta tesi inclouen avenços en l'automatització de tasques de RE i la millora de l'exactitud, l'eficiència i l'adopció d'eines i mètodes en el camp de NLP4RE. En definitiva, aquesta tesi pretén oferir noves perspectives, metodologies i contribucions tècniques al camp de NLP4RE.(Español) La ingeniería de requisitos (RE) es fundamental para el desarrollo exitoso de software, especialmente en proyectos modernos a gran escala. La gestión eficiente de artefactos basados en texto es clave para la correcta obtención, refinamiento y validación de los requisitos. A pesar de la tendencia industrial hacia la adopción de métodos de procesamiento de lenguaje natural (NLP), persisten desafíos en cuanto a su adopción, fiabilidad, escalabilidad y reutilización. Además, la aparición de grandes modelos de lenguaje (LLMs) ha sentado las bases para futuras investigaciones en análisis automatizado de documentos en el campo de la RE. Esta tesis explora la integración de métodos y herramientas de NLP para automatizar y mejorar las tareas de RE (NLP4RE) en tres áreas orientadas a documentos: trazabilidad de requisitos, análisis de requisitos para la recuperación de información y recopilación de retroalimentación de requisitos. Para la trazabilidad de requisitos, se proponen y evalúan métodos para la detección de dependencias y duplicados en documentos de requisitos basados en texto. En el análisis de requisitos, se desarrolla un enfoque basado en grafos de conocimiento para crear repositorios adaptativos y colaborativos de documentos relacionados con RE. Para la recopilación de retroalimentación de requisitos, se presentan y evalúan técnicas para la extracción de características de reseñas de usuarios y el análisis de dicha retroalimentación. Esta investigación se enmarca en el contexto de múltiples casos de estudio y estudios de muestra validados empíricamente, demostrando su efectividad en escenarios del mundo real. Las contribuciones presentadas en esta tesis implican avances en la optimización de las tareas de RE y la mejora en la precisión, eficiencia y adopción de herramientas y métodos NLP4RE. En última instancia, esta tesis tiene como objetivo proporcionar nuevas perspectivas, metodologías y contribuciones técnicas al campo de NLP4RE.Postprint (published version

Overcoming Language Dichotomies: Toward Effective Program Comprehension for Mobile App Development

Mobile devices and platforms have become an established target for modern software developers due to performant hardware and a large and growing user base numbering in the billions. Despite their popularity, the software development process for mobile apps comes with a set of unique, domain-specific challenges rooted in program comprehension. Many of these challenges stem from developer difficulties in reasoning about different representations of a program, a phenomenon we define as a "language dichotomy". In this paper, we reflect upon the various language dichotomies that contribute to open problems in program comprehension and development for mobile apps. Furthermore, to help guide the research community towards effective solutions for these problems, we provide a roadmap of directions for future work.Comment: Invited Keynote Paper for the 26th IEEE/ACM International Conference on Program Comprehension (ICPC'18

Dynamic Computation of Connectivity Data

Ytelsen til den underliggende kommunikasjonsteknologien spiller en viktig rolle i Vehicular Networks (VNs). Strenge krav må oppfylles for å sikre nødvendig sikkerhet og robusthet, i tillegg til å gi brukertilfredshet. I virkeligheten er det derimot ofte slik at tilgjengelig ytelse ikke oppfyller disse kravene. For eksempel, i distriktene kan mobilnettet fortsatt være basert på 2G- eller 3G-teknologi, og dødsoner kan forekomme. I denne oppgaven fokuserer vi på mobilnettet som kommunikasjonsmodus i VNs, med vekt på slike dødsoner. En klient-server-løsning der mobilnettverksdata leveres av målende klienter har blitt utviklet. Nettverksdata blir samlet inn ved bruk av Android-enheter og lastet opp til en sentral server. Enhetene kan brukes i forskjellige transportmidler, som biler og sykler, men også av fotgjengere. I sanntid prosesserer og aggregerer serveren dataene om til strukturerte dataformat, som også kan presenteres som grafer og geografiske kart som viser dødsoner og andre områder med dårlig forbindelse. Transportmiddel kan deretter forespørre disse aggregerte formene av data, og bruke dem til å avgjøre hvordan det skal kommuniseres og med hvilken tjenestekvalitet. For å bygge og teste systemet ble det gjort målinger fra to forskjellige geografiske områder i Norge: Lofoten og Trondheim. Over 130 000 målinger ble samlet fra forskjellige typer nettverk. En dybdeanalyse av disse målingene er gitt. Det resulterende systemet er en skalerbar løsning for å håndtere store volum av måledata fra forskjellige typer kilder

Quality of experience and access network traffic management of HTTP adaptive video streaming

The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.Die Doktorarbeit beschäftigt sich mit Quality of Experience (QoE) – der subjektiv empfundenen Dienstgüte – von adaptivem HTTP Videostreaming (HAS) und mit Verkehrsmanagement, das in Zugangsnetzwerken eingesetzt werden kann, um die QoE des adaptiven Videostreamings zu verbessern. Zuerst wurde der Einfluss von Adaptionsparameters und der Zeit pro Qualitätsstufe auf die QoE von adaptivem Videostreaming mittels subjektiver Crowdsourcingstudien untersucht. Die Ergebnisse wurden benutzt, um die QoE-optimale Adaptionsstrategie für gegebene Videos und Netzwerkbedingungen zu berechnen. Dies ermöglicht Dienstanbietern von Videostreaming verbesserte Adaptionsstrategien für adaptives Videostreaming zu entwerfen und zu benchmarken. Weiterhin untersuchte die Arbeit Konzepte zum Überwachen von QoE von Videostreaming in der Applikation und im Netzwerk, die von Netzwerkbetreibern im Kreislauf des QoE-bewussten Verkehrsmanagements eingesetzt werden können. Außerdem wurde eine analytische und simulative Leistungsbewertung von QoE-bewusstem Verkehrsmanagement auf einer Engpassverbindung durchgeführt. Schließlich untersuchte diese Arbeit sozialbewusstes Verkehrsmanagement für adaptives Videostreaming mittels WLAN Offloading, also dem Auslagern von mobilen Videoflüssen über WLAN Netzwerke. Es wurde ein Modell für die Verteilung von öffentlichen WLAN Zugangspunkte und eine Plattform für sozialbewusstes Verkehrsmanagement auf privaten, häuslichen WLAN Routern vorgestellt. Abschließend untersuchte eine simulative Leistungsbewertung den Einfluss von WLAN Offloading auf die QoE und den Energieverbrauch von mobilem adaptivem Videostreaming

A data-driven methodology for prioritizing traffic signal retiming operations

Signal retiming is one of the chief responsibilities of municipal transportation agencies and is an important means for reducing congestion and improving transportation quality and reliability. Many agencies conduct signal retiming and adjustment in a schedule-based manner. However, leveraging a data-driven, need-based approach to the prioritization of signal retiming operations could better optimize use of agency resources. Additionally, the growing availability of probe vehicle data has made it an increasingly popular tool for use in roadway performance measurement. This thesis presents a methodology for utilizing segment-level probe-based speed data to rank the performance of traffic signal corridors for retiming purposes. This methodology is then demonstrated in an analysis of 79 traffic signal corridors maintained by the City of Austin, Texas. The analysis considers 15-minute speed records for all weekdays in September 2016 and September 2017 to compute metrics and rank corridors based on their relative performance across time periods. The results show that the ranking methodology compares corridors equitably despite differences in road length, functional class, and traffic signal density. Additionally, results indicate that the corridors prioritized by the ranking methodology represent a much greater potential for improving travel time than the corridors selected under the schedule-based approach. This methodology is then packaged into a web-based tool for integration into agency decision-making. Finally, consideration is given to how this methodology might be used to identify candidate corridors for implementing adaptive signal control techniques.Civil, Architectural, and Environmental Engineerin

Building Adaptive Capacity: An Analysis of Innovations in Information and Communication Technology in Post-Earthquake Haiti

New information and communication technology (ICT) platforms that emerged in the humanitarian response to the 2010 earthquake in Port-au-Prince, Haiti, have been hyped for their ability to spatialize and coordinate disaster relief efforts and, more broadly, to assist short-term mitigation efforts. This research examines the role of recent innovations in ICT in augmenting the adaptive capacity of the Haitian state to carry out mitigation and adaptation planning. Case studies, analysis of communication flows, and interviews were used to assess the state uptake of three different mobile and open source platforms, including: Ushahidi/Noula, SIS-KLOR, and OpenStreetMap. Findings suggest while these new platforms do offer the potential to increase state efficiencies, assess community need, and produce geospatial information, a combination of fear of responsibility, limited resources, lack of local ownership, and path dependency render foreign technologies unsuitable for Haitian use and impede institutionalization. Recommendations focus on shifting practice from investing in foreign technologies to promoting domestic innovation in ICT by strengthening and updating intellectual property legislation and investing in domestic tech firms. This research is relevant to strategic planners, policy-makers, and international organizations working in the IT secto