The literature dedicated to free and open source software emphasizes the support given by the community to software producers. However, the community is also a place of conflict and can sometimes experience violent splits (forks). Communities can show different forms of resistance to change. In this research, we propose a re-reading of these mechanisms of opposition in light of Albert Hirschman's theory (exit, voice, loyalty). We present the fork as a new form of defection (exit) allowed by licenses and discuss the rationality of choice for the economic actors who implement it.
Customizability, extensive community support and ease of availability have led to the popularity of Open-Source Software (OSS) systems. However, maintenance of these systems is a challenge especially as they become considerably large and complex with time. One possible method of ensuring effective quality in large scale OSS is the adoption of software change prediction models. These models aid in identifying change-prone parts in the early stages of software development, which can then be effectively managed by software practitioners. This study extensively evaluates eight Homogeneous Ensemble Learners (HEL) for developing software change prediction models on five large scale OSS datasets. HEL, which integrate the outputs of several learners of the same type are known to generate improved results than other non-ensemble classifiers. The study also statistically compares the results of the models developed by HEL with ten non-ensemble classifiers. We further assess the change in performance of HEL for developing software change prediction models by substituting their default base learners with other classifiers. The results of the study support the use of HEL for developing software change prediction models and indicate Random Forest as the best HEL for the purpose.
Software development for electronic games has remarkable performance and portability requirements, and the system and low-level languages usually provide those. This ecosystem became homogeneous at commercial levels around C and C++, both for open source or proprietary solutions. However, innovations brought other possibilities that are still growing in this area, including Rust and other system languages. Rust has low-level language properties and modern security guarantees in access to memory, concurrency, dependency management, and portability. The Open Source game engine Amethyst has become a reference solution for game development in Rust, has a large and active community, and endeavors in being an alternative to current solutions. Amethyst brings parallelism and performance optimizations, with the advantages of the Rust language. This paper presents scripting concepts that allow the game logic to be implemented in an external interpreted language. We present a scripting module called Legion Script that was implemented for the entity and component system (ECS) called Legion, part of the Amethyst organization. As a Proof-of-Concept (POC), we perform the Python code interpretation using the Rust Foreign Function Interface (FFI) with CPython. This POC added scripting capabilities to Legion. We also discuss the benefit of using the alternative strategy of developing a POC before contributing to OSS communities in emergent technologies.
Background. Industrial software increasingly relies on open source software. Therefore, industrial practitioners need to evaluate the quality of a specific open source product they are considering for adoption. Automated tools greatly help assess open source software quality, by reducing the related costs, but do not provide perfectly reliable indications. Indications from tools can be used to restrict and focus manual code inspections, which are typically expensive and time-consuming, only on the code sections most likely to contain faults. Aim. We investigate the extent of the effectiveness of static analysis bug detectors by themselves and in combination with code smell detectors in guiding inspections. Method. We performed an empirical study, in which we used a bug detector (SpotBugs) and a code smell detector (JDeodorant). Results. Our results show that the selected bug detector is precise enough to justify inspecting the code it flags as possibly buggy. Applying the considered code smell detector makes predictions even more precise, but at the price of a rather low recall. Conclusions. Using the considered tools as inspection drivers proved quite useful. The relatively small size of our study does not allow us to draw universally valid conclusions, but our results should be applicable to source code of any kind, although they were obtained from open source code.
Formulation of mandatory requirements in procurement projects has significant influence on opportunities for development and deployment of Open Source Software (OSS). The paper contributes insights on a widespread practice amongst public procurement projects which causes problematic lock-in effects and thereby inhibits opportunities for use of OSS solutions. Through a systematic investigation of 30 randomly selected procurement projects in the software domain the paper highlights illustrative examples of mandatory requirements which cause lock-in and presents five recommendations for how requirements instead should be formulated in order to avoid causing lock-in. Findings show significant lock-in caused by current procurement practices with a stark preference for proprietary software and SaaS solutions amongst procuring organisations.
Finding the same or similar code snippets in the source code for a query code snippet is one of the fundamental activities in software maintenance. Code clone detectors detect the same or similar code snippets, but they report all of the code clone pairs in the target, which are generally excessive to the users. In this paper, we propose ccgrep, a token-based pattern matching tool with the notion of code clone pairs. The user simply inputs a code snippet as a query and specifies the target source code, and gets the matched code snippets as the result. The query and the result snippets form clone pairs. The use of special tokens (named meta-tokens) in the query allows the user to have precise control over the matching. It works for the source code in C, C++, Java, and Python on Windows or Unix with practical scalability and performance. The evaluation results show that ccgrep is effective in finding intended code snippets in large Open Source Software.
Open source software (OSS), playing an increasingly critical role nowadays, has been commonly adopted and integrated in various software products. For many practitioners, selecting and adopting suitable OSS can help them greatly. Though many studies have been conducted on proposing OSS evaluation and selection models, a limited number are followed and used in the industry. Meanwhile, many existing OSS evaluation tools, though providing valuable details, fall short on offering intuitive suggestions in terms of framework-supported evaluation factors. Towards filling the gap, we propose an Open Source Software Project Evaluation and Selection TOol (OSS PESTO). Targeting OSS on Github, the largest OSS source code host, it facilitates the evaluation practice by enabling practitioners to compare candidates therein in terms of selected OSS evaluation models. It also allows in-time Github data collection and customized evaluation that enriches its effectiveness and ease of use.
In this work we are studying the sentiment in Open Source Software projects and more specifically in the process of bug reporting, to investigate the human factor, namely, the feedback from the community (end-users, developers, testers, etc.). One of the characteristics for which Open Source Software has gained attention, over the years, is the fact that it is continuously being tested and maintained by its community of volunteers. Sentiment analysis, a rapidly growing field, can enrich software evaluation with a social aspect. Results suggest that FLOSS projects’ bug reports can potentially constitute a rich emotionally - imbued information source.
Increasing amount of data the organizations worldwide have at their disposal lead to the need to structure, organize and present the information obtained from it. That is because, in today’s rapid-changing business environment, managers and executives need to be able to gain crucial insights about the ongoing project in as little time as possible. Recently, energy efficiency has become a greater field of research, and companies started concentrating on monitoring energy-related metrics. In addition, many of them have built their own internal tools (dashboards) to do just this. However, one of the major drawbacks of building specialized tools is the lack of adaptability. That is, they are often tailored to only one person (e.g. CEO), or a small group of them (e.g. board of directors, managers). Furthermore, the combination of metrics that is displayed to them does not change over time. This is a problem because most likely there exists a better metric combination that would allow users to get the crucial insights faster. To fill this gap, our ongoing research focuses on making the dashboards adaptable to multiple roles within the organization while optimizing for a certain goal. In some scenarios the dashboard’s goal may be to detect defects, in others it may be to generate the most profit. As our primary research interest is to amplify energy efficiency, we have chosen that to be our dashboard’s goal. Our previous work suggests that in order to handle compound metrics at scale it is needed to represent the dashboard as a complex system. This paper presents the design and the architecture of our proposed solution synergizing the notions from complexity theory, software architecture and user experience (UX) design.
Intelligent systems of simulation become a key stage of the scheduling of companies and industries work. Most of the existing decision support systems are desktop software. Today there is a need to use durability, flexibility, availability and crossplatforming information technologies. The paper proposes the idea of working cloud based decision support system BPsim.Web and this one consists of some set of services and tools. The model of the multiagent resources conversion process is considered. The process of the simulation model developing via BPsim.Web is described. An example of the real process model is given.
Increasing development complexity in software applications raises major concerns about technical debt management, also in Open Source environments. A strategic management perspective provides organizations with an action map to pursue business’ targets with limited resources. This article presents the Strategic Technical Debt Management Model (STDMM) to provide practitioners with an actionable roadmap to manage their technical debt properly, considering both social and technical aspects. To do so, we pursued a theoretical mapping, exploiting a set of interviews of 124 carefully selected and well-informed domain experts of the IT financial sector.
Tools are of paramount importance in automating software engineering tasks; although the Agile Manifesto prefers “individuals and their interactions over processes and tools”, some agile development activities make no exception and can be automated effectively and successfully. In process frameworks like Scrum or similar ones some activities are in fact quite structured and need specific tool support. Hence, it is interesting to study the combination of specific agile practices with OSS tools. In this paper we introduce the Compositional Agile System (CAS), an environment created to support iAgile and automate some of its tasks using OSS tools. iAgile is a Scrum-like model designed to develop critical systems in the military domain .
Companies without expertise in software development can opt to form consortia to develop open source software to meet their needs, as an alternative to the build-or-buy decision. Such user-led foundations are little understood, due to a limited number of published examples. In particular, almost nothing is known about the ecosystems surrounding user-led foundations. Our work seeks to address this gap, through an exploratory qualitative survey of openKONSEQUENZ, from the German energy sector. We find that the technological goals are quite homogeneous, independent of a participant’s role in the ecosystem, but that economic conflicts exist between foundation members and supplier companies due to the consortium’s efforts to transform the software market structure to limit dependency on specific vendors.
An exact query is a query in which the user specifies precisely what to retrieve from a database (XML or relational database). For these queries only data that strictly respect all user's conditions is returned. XML documents are generally semi-structured. Due to the non-existence or lack of knowledge of the model of the document being queried, when exact queries are used, there is a high risk of obtaining an empty result (in the case of too specific queries) or too large (in the case of too vague queries). In contrast to exact queries, requests with preferences aim to return only the most relevant results in order to avoid empty or too important results as much as possible. To achieve this goal, requests with preferences generally consist of two parts: the first part is used to express strict constraints and the second part to express preferences or wishes. The satisfaction of both parts increases the relevance of the corresponding results. This paper presents XQuery preference, an extension of the XQuery language, that allows to express requests with preferences relating to both the values and the structure of an XML document. A representation model of such requests based on the Generalized Tree Pattern (GTP) model is also proposed in order to allow an evaluation of these requests through a tree pattern matching process. Integration of the proposed language in open source implementations of XQuery like BaseX, Berkeley DB XML, eXist-db, Galax and much more, will allow users to get much more relevant responses to their concerns.
Corpus data is widely used to solve different linguistic, educational and applied problems. The Tatar corpus management system (http://tugantel.tatar) is specifically developed for Turkic languages. The functionality of our corpus management system includes a search of lexical units, morphological and lexical search, a search of syntactic units, a search of N-grams and others. The search is performed using open source tools (database management system MariaDB, Redis data store). This article describes the process of choosing FLOSS for the main components of our system and also processing a search query and building a linked open dataset based on corpus data.
Companies without expertise in software development can opt to form consortia to develop open source software to meet their needs, as an alternative to the build-or-buy decision. Such user-led foundations are little understood, due to a limited number of published examples. In particular, almost nothing is known about the ecosystems surrounding user-led foundations. Our work seeks to address this gap, through an exploratory qualitative survey of openKONSEQUENZ, from the German energy sector. We find that the technological goals are quite homogeneous, independent of a participant’s role in the ecosystem, but that economic conflicts exist between foundation members and supplier companies due to the consortium’s efforts to transform the software market structure to limit dependency on specific vendors.
This article discusses the idea of developing an intelligent and customizable automated system for real-time text and voice dialogs with the user. This system can be used for almost any subject area, for example, to create an automated robot - a call center operator or smart chat bots, assistants, and so on. This article presents the developed flexible architecture of the proposed system. The system has many independent submodules. These modules work as interacting microservices and use several speech recognition schemes, including a decision support submodule, third-party speech recognition systems and a post-processing subsystem. In this paper, the post-processing module of the recognized text is presented in detail on the example of Russian and English dictionary models. The proposed submodule also uses several processing steps, including the use of various stemming methods, the use of word stop-lists or other lexical structures, the use of stochastic keyword ranking using a weight table, etc.
Samsung Electronics and Microsoft Corporation have been developing open source implementation of .NET platform called .NET Core since 2016. This platform is usually used for implementation of server-side and desktop applications, and Samsung has also adopted .NET Core virtual machine and libraries for Tizen OS. This solution was integrated into various ARM CPU based devices e.g. smart watches, TVs and other electronic devices. Tizen has always supported variety of languages and SDKs for developers. .NET has greatly expanded this variety by supporting new tools and new languages. This paper describes major challenges that we have encountered during integration of .NET to Tizen OS as well as optimizations, which were applied to .NET Core to make applications startup and memory consumption better on variety of devices.
Maintainability is a key factor for the evolution of an open source system due to the highly distributed development teams that contribute to many projects. In the literature there are a number of different approaches that has been developed to evaluate the maintainability of a product but almost each method has been developed in an independent way without leveraging on the existing work and with almost no independent evaluation of the performance of the models. In most of the cases, the models are only validated through a limited set of projects only by the people that propose the specific approach. This paper is a first step towards a different direction focusing on the independent application of the existing models to popular open source projects.
The possibility of the boundaries detection in the images of crushed ore particles using a convolutional neural network is analyzed. The structure of the neural network is given. The construction of training and test datasets of ore particle images is described. Various modifications of the underlying neural network have been investigated. Experimental results are presented.
