Process software simulation model of Lean-Kanban Approach

Software process simulation is important for reducing errors, helping analysis of the risks and for improving software quality. In recent years, the Lean-Kanban approach has been widely applied in software practice including software development and maintenance. The Lean-Kanban approach minimizes the Work-In-Progress (WIP), which is the number of items that are worked on by the team at any given time. It has been demonstrated that such approach can help to improve software maintenance and development processes in industrial environments. The goal of the simulation model itself is to increase the understanding and to support decisions for planning such kind of projects. Considering the threats to validity of the study, the accuracy and reliability of the simulation model could be shown and the simulation model implementation allows for deriving hypothesis on the impact of distribution on parameters such as throughput. In this thesis, we describe our simulation studies, which show that the Lean-Kanban approach can indeed help to reduce the average time needed to complete maintenance or development issues. This simulation model can simulate existing maintenance and development processes that does not use a WIP limit, as well as a maintenance and development processes that adopt a WIP limit. We performed some case studies using real data collected from different projects. The results confirmthat the WIP-limited process as advocated by the Lean- Kanban approach could be useful to increase the efficiency of software maintenance and development, as reported in previous industrial practices

A Synchrosqueezed Wavelet Transform assisted machine learning framework for time series forecasting Contributions to KDWEB poster session, a.d. 2016

Abstract. The attention of researchers in the field of signal analysis has recently been captured by several kinds of reassignment techniques. Among the reallocation methods the Synchrosqueezing approach maps a continuous wavelet transform from the time-scale to the time-frequency plane, allowing a much more definite and consistent representation of the frequency content of a signal. Its mathematical foundations prove that the Synchrosqueezing Wavelet Transform is directly related to the Empirical Mode Decomposition -EMD, since both methods allow the decomposition of a signal having finite energy in its intrinsic mode functions, each having time-varying frequency and amplitude. We develop a fast 1 C++ implementation of the Synchrosqueezed Wavelet Transform -SST suitable for the synchronic extrusion of instantaneous frequency information from univariate time series. Such module, totally configurable and adaptable to input datasets having different statistical properties, can be coupled with a predictor system to the purpose of real value forecasting. We project such a composite application and plan to research the forecasting accuracy achievable, using different types of non parametric statistical estimators or neural regressors

The future of Bitcoin: a Synchrosqueezing Wavelet Transform to predict search engine query trends Contributions to KDWEB Conference, a.d. 2016

Abstract. In recent years search engines have become the go-to methods for achieving many types of knowledge, spanning from detailed descriptions or general information interesting to the user. Likewise several reassignment techniques are capturing the attention of researchers in the field of signal analysis. Particularly, the Synchrosqueezing Wavelet Transform -SST allows signal decomposition and instantaneous frequency extrusion, at the same time promising consistent reconstruction capabilities, hence the possibility to contrive an SST assisted inference engine. We are going to test it using datasets extracted from search engine trends, using a cloud of keywords related to the Bitcoin topic. This could be useful to study the evolution of the cryptocurrency both in time and geographical terms, and to estimate the future number of queries. The importance of Bitcoin queries prediction goes beyond the academic and research environments and, as such, it could lead to valuable commercial applications, such as financial recommender systems or blockchain-based transaction managers development

A real-time FFT-KLT implementation for SETI research at the Sardinia Radio Telescope

The Search for ExtraTerrestrial Intelligence (SETI) is a project whose goal is to find possible life signatures emitted (intentionally or unintentionally) by possible civilizations from other habitable planets. Historically, the narrow-band FFT approach has been used, since a quasi-monochromatic signal is the most probable signal one would use to send a message to another world, that is in the case of intentionally- transmitted signals. Nevertheless, we could receive an unintentionally-transmitted signal as well. In that case, it would most certainly not be a quasi-monochromatic signal, but would probably be similar (with a wider bandwidth, of the order of MHz) to the signals that we use for conventional communications on Earth. The Kahrunen-Loève Transform (KLT) is a powerful algorithm for such a kind of research. However, a real-time implementation of the KLT has thus far not worked due to a lack of technological resources. We describe a hardware-software infrastructure at the Sardinia Radio Telescope (SRT) that, in real-time, makes it possible to perform the KLT in parallel to the FFT

Software Startups -- A Research Agenda

Software startup companies develop innovative, software-intensive products within limited time frames and with few resources, searching for sustainable and scalable business models. Software startups are quite distinct from traditional mature software companies, but also from micro-, small-, and medium-sized enterprises, introducing new challenges relevant for software engineering research. This paper's research agenda focuses on software engineering in startups, identifying, in particular, 70+ research questions in the areas of supporting startup engineering activities, startup evolution models and patterns, ecosystems and innovation hubs, human aspects in software startups, applying startup concepts in non-startup environments, and methodologies and theories for startup research. We connect and motivate this research agenda with past studies in software startup research, while pointing out possible future directions. While all authors of this research agenda have their main background in Software Engineering or Computer Science, their interest in software startups broadens the perspective to the challenges, but also to the opportunities that emerge from multi-disciplinary research. Our audience is therefore primarily software engineering researchers, even though we aim at stimulating collaborations and research that crosses disciplinary boundaries. We believe that with this research agenda we cover a wide spectrum of the software startup industry current needs

A real-time KLT implementation for radio-SETI applications

SETI, the Search for ExtraTerrestrial Intelligence, is the search for radio signals emitted by alien civilizations living in the Galaxy. Narrow-band FFT-based approaches have been preferred in SETI, since their computation time only grows like N*lnN, where N is the number of time samples. On the contrary, a wide-band approach based on the Kahrunen-Lo`eve Transform (KLT) algorithm would be preferable, but it would scale like N*N. In this paper, we describe a hardware-software infrastructure based on FPGA boards and GPU-based PCs that circumvents this computation-time problem allowing for a real-time KLT

Is bitcoin’s market predictable? Analysis of Web search and social media

In recent years, Internet has completely changed the way real life works. In particular, it has been possible to witness the online emergence of web 2.0 services that have been widely used as communication media. On one hand, services such as blogs, tweets, forums, chats, email have gained wide popularity. On the other hand, due to the huge amount of available information, searching has become dominant in the use of Internet. Millions of users daily interact with search engines, producing valuable sources of interesting data regarding several aspects of the world. Bitcoin, a decentralized electronic currency, represents a radical change in financial systems, attracting a large number of users and a lot of media attention. In this work we studied whether Bitcoin’s trading volume is related to the web search and social volumes about Bitcoin. We investigated whether public sentiment, expressed in large-scale collections of daily Twitter posts, can be used to predict the Bitcoin market too. We achieved significant cross correlation outcomes, demonstrating the search and social volumes power to anticipate trading volumes of Bitcoin currency

Blockchain oracles for document certification: A case study

The use of blockchain oracles is becoming increasingly widespread as it responds to the problem of providing the blockchain with external data automatically. In this paper, we focus on the use of blockchain oracles for the certification of articulated data. In particular, we show how it is possible to design and implement a DApp capable of providing documents on request without these data permanently residing on the blockchain. We present a case study of the on-demand provision of university curriculum, fully managed via a blockchain oracle. In this system, the smart contract will not record data permanently but only for the time necessary for its use. The system is intended to allow a user to request and obtain data only about herself. The paper also presents a classification of the types of blockchain oracles and their most common applications. The case study will be presented as blockchain-oriented software, and in terms of architecture and dynamics using UML diagrams, the benefits and drawbacks of the approach are discussed