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Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.
Chief Editor, Professor Tramontana, is based at the University of Catania and his research primarily concerns the areas of software engineering and distributed systems.
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A Tool-Based Perspective on Software Code Maintainability Metrics: A Systematic Literature Review
Software maintainability is a crucial property of software projects. It can be defined as the ease with which a software system or component can be modified to be corrected, improved, or adapted to its environment. The software engineering literature proposes many models and metrics to predict the maintainability of a software project statically. However, there is no common accordance with the most dependable metrics or metric suites to evaluate such nonfunctional property. The goals of the present manuscript are as follows: (i) providing an overview of the most popular maintainability metrics according to the related literature; (ii) finding what tools are available to evaluate software maintainability; and (iii) linking the most popular metrics with the available tools and the most common programming languages. To this end, we performed a systematic literature review, following Kitchenham’s SLR guidelines, on the most relevant scientific digital libraries. The SLR outcome provided us with 174 software metrics, among which we identified a set of 15 most commonly mentioned ones, and 19 metric computation tools available to practitioners. We found optimal sets of at most five tools to cover all the most commonly mentioned metrics. The results also highlight missing tool coverage for some metrics on commonly used programming languages and minimal coverage of metrics for newer or less popular programming languages. We consider these results valuable for researchers and practitioners who want to find the best selection of tools to evaluate the maintainability of their projects or to bridge the discussed coverage gaps for newer programming languages.
Internet Penetration and Regional Financial Development in China: Empirical Evidence Based on Chinese Provincial Panel Data
The Internet has revolutionized the patterns of financial development and economic growth. To assess the impacts of internet penetration on the financial industry, this paper analyzed ten-year Chinese provincial panel data and concluded that regional Internet penetration accelerates financial development. Furthermore, the efficiency of Internet investment in underdeveloped provinces is better than that in developed provinces. More meaningfully, Internet penetration promotes the transparency of the securities market and regional financial participation. This indicates that Internet technology facilitates the advancement of the finance industry and the securities market.
How to Evaluate the Productivity of Software Ecosystem: A Case Study in GitHub
With the development of open source community, the software ecosystem has become a popular perspective in the research on software development process and environment. Software productivity is an important evaluation indicator of the software ecosystem health. A successful software ecosystem relies on long-term and stable production activities by the users, which ensures that the software ecosystem can continuously provide the value needed by users. Therefore, the measurement of software ecosystem productivity can help maintain the user development efficiency and the stability of the software ecosystem. However, there is still little literature on the productivity of open source software ecosystems. By analogy with the natural ecosystem, this paper gives the relevant definitions of software ecosystem productivity and analyzes the factors affecting the productivity of software ecosystem. Based on the factors of the ecosystem productivity and their interrelationships, this paper establishes a software ecosystem productivity model and takes the GitHub platform as an example for detailed analysis and explanation. The results show that the model can better explain the factors affecting the productivity of software ecosystems. It is helpful for the research on the measurement of the software ecosystem health and the software development efficiency.
Analysis of the Impact of Interest Rate Liberalization on Financial Services Management in Chinese Commercial Banks
With the advancement of China's interest rate marketization reform, commercial banks' net interest margin has narrowed. This paper selects 16 representative listed banks as the research object and conducts an empirical analysis from the two dimensions: profit level and profit structure. The study finds that the marketization of interest rates promoted the narrowing of net interest margins caused by the narrowing of net interest margins, and the profitability of commercial banks was suppressed. The narrowing of net interest spreads forced commercial banks to actively expand their intermediate business activities and adjust business structure correspondingly. The narrowing of net interest spreads has different impacts on the profitability of commercial banks of different sizes.
Using Natural Language Preprocessing Architecture (NLPA) for Big Data Text Sources
During the last years, big data analysis has become a popular means of taking advantage of multiple (initially valueless) sources to find relevant knowledge about real domains. However, a large number of big data sources provide textual unstructured data. A proper analysis requires tools able to adequately combine big data and text-analysing techniques. Keeping this in mind, we combined a pipelining framework (BDP4J (Big Data Pipelining For Java)) with the implementation of a set of text preprocessing techniques in order to create NLPA (Natural Language Preprocessing Architecture), an extendable open-source plugin implementing preprocessing steps that can be easily combined to create a pipeline. Additionally, NLPA incorporates the possibility of generating datasets using either a classical token-based representation of data or newer synset-based datasets that would be further processed using semantic information (i.e., using ontologies). This work presents a case study of NLPA operation covering the transformation of raw heterogeneous big data into different dataset representations (synsets and tokens) and using the Weka application programming interface (API) to launch two well-known classifiers.
Evolutionary Game Models on Multiagent Collaborative Mechanism in Responsible Innovation
Innovation is a game process; in particular, the behavior among multiple agents in responsible innovation is susceptible to the influence of benefits, risks, responsibilities, and other factors, resulting in unstable collaborative relationships. Therefore, this paper constructs a tripartite evolutionary game model including the government, enterprises, and the public, combined with system dynamics modeling to simulate and analyze the tripartite behavior strategy and sensitivity to relevant exogenous variables. The study shows that the tripartite game eventually converges to a stable state of the government active supervision, enterprises making responsible innovation, and the public’s positive participation. The positive participation of the public drives rapidly the game to a steady state, while the behavioral strategies of enterprises are more susceptible to the behavior of the government. Supervision cost, penalty amount, and value compensation are the most critical factors influencing the change of the corresponding agents’ behavior strategy, and the final strategic stability of tripartite is affected by multiple exogenous variables.