6 research outputs found
The ICO Phenomenon and Its Relationships with Ethereum Smart Contract Environment
Initial Coin Offerings (ICO) are public offers of new cryptocurrencies in
exchange of existing ones, aimed to finance projects in the blockchain
development arena. In the last 8 months of 2017, the total amount gathered by
ICOs exceeded 4 billion US$, and overcame the venture capital funnelled toward
high tech initiatives in the same period. A high percentage of ICOS is managed
through Smart Contracts running on Ethereum blockchain, and in particular to
ERC-20 Token Standard Contract. In this work we examine 1388 ICOs, published on
December 31, 2017 on icobench.com Web site, gathering information relevant to
the assessment of their quality and software development management, including
data on their development teams. We also study, at the same date, the financial
data of 450 ICO tokens available on coinmarketcap.com Web site, among which 355
tokens are managed on Ethereum blochain. We define success criteria for the
ICOs, based on the funds actually gathered, and on the behavior of the price of
the related tokens, finding the factors that most likely influence the ICO
success likeliness
Mining software repositories: measuring effectiveness and affectiveness in software systems.
Software Engineering field has many goals, among them we can certainly
deal with monitoring and controlling the development process in order to meet
the business requirements of the released software artifact. Software engineers
need to have empirical evidence that the development process and the overall
quality of software artifacts is converging to the required features. Improving
the development process's Effectiveness leads to higher productivity, meaning
shorter time to market, but understanding or even measuring the software de-
velopment process is an hard challenge. Modern software is the result of a
complex process involving many stakeholders such as product owners, quality
assurance teams, project manager and, above all, developers. All these stake-
holders use complex software systems for managing development process, issue
tracking, code versioning, release scheduling and many other aspect concerning
software development. Tools for project management and issues/bugs tracking
are becoming useful for governing the development process of Open Source soft-
ware. Such tools simplify the communications process among developers and
ensure the scalability of a project. The more information developers are able to
exchange, the clearer are the goals, and the higher is the number of developers
keen on joining and actively collaborating on a project.
By analyzing data stored in such systems, researchers are able to study and
address questions such as: Which are the factors able to impact the software
productivity? Is it possible to improve software productivity shortening the
time to market?.
The present work addresses two major aspect of software development pro-
cess: Effectiveness and Affectiveness. By analyzing data stored in project man-
agement and in issue tracking system of Open Source Communities, we mea-
sured the Effectiveness as the time required to resolve an issue and analyzed
factors able to impact it
Mining software repositories: measuring effectiveness and affectiveness in software systems.
Software Engineering field has many goals, among them we can certainly
deal with monitoring and controlling the development process in order to meet
the business requirements of the released software artifact. Software engineers
need to have empirical evidence that the development process and the overall
quality of software artifacts is converging to the required features. Improving
the development process's Effectiveness leads to higher productivity, meaning
shorter time to market, but understanding or even measuring the software de-
velopment process is an hard challenge. Modern software is the result of a
complex process involving many stakeholders such as product owners, quality
assurance teams, project manager and, above all, developers. All these stake-
holders use complex software systems for managing development process, issue
tracking, code versioning, release scheduling and many other aspect concerning
software development. Tools for project management and issues/bugs tracking
are becoming useful for governing the development process of Open Source soft-
ware. Such tools simplify the communications process among developers and
ensure the scalability of a project. The more information developers are able to
exchange, the clearer are the goals, and the higher is the number of developers
keen on joining and actively collaborating on a project.
By analyzing data stored in such systems, researchers are able to study and
address questions such as: Which are the factors able to impact the software
productivity? Is it possible to improve software productivity shortening the
time to market?.
The present work addresses two major aspect of software development pro-
cess: Effectiveness and Affectiveness. By analyzing data stored in project man-
agement and in issue tracking system of Open Source Communities, we mea-
sured the Effectiveness as the time required to resolve an issue and analyzed
factors able to impact it
On the influence of maintenance activity types on the issue resolution time
The ISO/IEC 14764 standard specifies four types of software maintenance activities spanning the different motivations that software engineers have while performing changes to an existing software system. Undoubtedly, this classification has helped in organizing the workow within software projects, however for planning purposes the relative time differences for the respective tasks remains largely unexplored. In this empirical study, we investigate the inuence of the maintenance type on issue resolution time. From GitHub's issue repository, we analyze more than 14000 issue reports taken from 34 open source projects and classify them as corrective, adaptive, perfective or preventive maintenance. Based on this data, we show that the issue resolution time depends on the maintenance type. Moreover, we propose a statistical model to describe the distribution of the issue resolution time for each type of maintenance activity. Finally, we demonstrate the usefulness of this model for scheduling the maintenance workload. Copyright 2014 ACM