In a variety of open source software projects, we document a superlinear growth of production intensity () as a function of the number of active developers , with a median value of the exponent , with large dispersions of from slightly less than up to . For a typical project in this class, doubling of the group size multiplies typically the output by a factor , explaining the title. This superlinear law is found to hold for group sizes ranging from 5 to a few hundred developers. We propose two classes of mechanisms, interaction-based and large deviation, along with a cascade model of productive activity, which unifies them. In this common framework, superlinear productivity requires that the involved social groups function at or close to criticality, or in a “superradiance” mode, in the sense of the appearance of a cooperative process and order involving a collective mode of developers defined by the build up of correlation between the contributions of developers. In addition, we report the first empirical test of the renormalization of the exponent of the distribution of the sizes of first generation events into the renormalized exponent of the distribution of clusters resulting from the cascade of triggering over all generation in a critical branching process in the non-meanfield regime. Finally, we document a size effect in the strength and variability of the superlinear effect, with smaller groups exhibiting widely distributed superlinear exponents, some of them characterizing highly productive teams. In contrast, large groups tend to have a smaller superlinearity and less variability.

Access to data stored in software repositories by systems such as version control, bug and issue tracking, or mailing lists is essential for assessing the quality of a software system. A myriad of analyses exploiting that data have been proposed throughout the years: source code analysis, code duplication analysis, co-change analysis, bug prediction, or detection of bug fixing patterns. However, easy and straight forward synergies between these analyses rarely exist. To tackle this problem we have developed SOFAS, a distributed and collaborative software analysis platform to enable a seamless interoperation of such analyses. In particular, software analyses are offered as RESTful web services that can be accessed and composed over the Internet. SOFAS services are accessible through a software analysis catalog where any project stakeholder can, depending on the needs or interests, pick specific analyses, combine them, let them run remotely and then fetch the final results. That way, software developers, testers, architects, or quality assurance experts are given access to quality analysis services. They are shielded from many peculiarities of tool installations and configurations, but SOFAS offers them sophisticated and easy-to-use analyses. This paper describes in detail our SOFAS architecture, its considerations and implementation aspects, and the current set of implemented and offered RESTful analysis services. Abstract-Access to data stored in software repositories by systems such as version control, bug and issue tracking, or mailing lists is essential for assessing the quality of a software system. A myriad of analyses exploiting that data have been proposed throughout the years: source code analysis, code duplication analysis, co-change analysis, bug prediction, or detection of bug fixing patterns. However, easy and straight forward synergies between these analyses rarely exist. To tackle this problem we have developed SOFAS , a distributed and collaborative software analysis platform to enable a seamless interoperation of such analyses. In particular, software analyses are offered as RESTful web services that can be accessed and composed over the Internet. SOFAS services are accessible through a software analysis catalog where any project stakeholder can, depending on the needs or interests, pick specific analyses, combine them, let them run remotely and then fetch the final results. That way, software developers, testers, architects, or quality assurance experts are given access to quality analysis services. They are shielded from many peculiarities of tool installations and configurations, but SOFAS offers them sophisticated and easy-to-use analyses. This paper describes in detail our SOFAS architecture, its considerations and implementation aspects, and the current set of implemented and offered RESTful analysis services.

The feature list of modern IDEs is growing steadily and mastering these tools becomes more and more demanding, especially for novice programmers. Despite their remarkable capabilities, IDEs often still cannot directly answer the questions that arise during program comprehension tasks. Instead developers have to map their questions to multiple concrete queries that can be answered only by combining several tools and examining the output of each of them manually to distill an appropriate answer. Existing approaches have in common that they are either limited to a set of predefined, hardcoded questions, or that they require to learn a specific query language only suitable for that limited purpose. We present a framework to query for information about a software system using guided-input natural language resembling plain English. For that, we model data extracted by classical software analysis tools with an OWL ontology and use knowledge processing technologies from the Semantic Web to query it. We also present a case study that demonstrates how our framework can be used to answer queries about static source code information for program comprehension purposes. Supporting Developers with Natural Language QueriesMichael Würsch, Giacomo Ghezzi, Gerald Reif, and Harald C. Gall {wuersch,ghezzi,reif,gall}@ifi.uzh.ch s.e.a.l. -software architecture and evolution lab Department of Informatics University of Zurich, Switzerland ABSTRACTThe feature list of modern IDEs is steadily growing and mastering these tools becomes more and more demanding, especially for novice programmers. Despite their remarkable capabilities, IDEs often still cannot directly answer the questions that arise during program comprehension tasks. Instead developers have to map their questions to multiple concrete queries that can be answered only by combining several tools and examining the output of each of them manually to distill an appropriate answer. Existing approaches have in common that they are either limited to a set of predefined, hardcoded questions, or that they require to learn a specific query language only suitable for that limited purpose. We present a framework to query for information about a software system using guided-input natural language resembling plain English.For that, we model data extracted by classical software analysis tools with an OWL ontology and use knowledge processing technologies from the Semantic Web to query it. We use a case study to demonstrate how our framework can be used to answer queries about static source code information for program comprehension purposes.

The Semantic Web provides a standardized, well-established framework to define and work with ontologies. It is especially apt for machine processing. However, researchers in the field of software evolution have not really taken advantage of that so far.In this paper, we address the potential of representing software evolution knowledge with ontologies and Semantic Web technology, such as Linked Data and automated reasoning.We present SEON, a pyramid of ontologies for software evolution, which describes stakeholders, their activities, artifacts they create, and the relations among all of them. We show the use of evolution-specific ontologies for establishing a shared taxonomy of software analysis services, for defining extensible meta-models, for explicitly describing relationships among artifacts, and for linking data such as code structures, issues (change requests), bugs, and basically any changes made to a system over time.For validation, we discuss three different approaches, which are backed by SEON and enable semantically enriched software evolution analysis. These techniques have been fully implemented as tools and cover software analysis with web services, a natural language query interface for developers, and large-scale software visualization. Abstract The Semantic Web provides a standardized, well-established framework to define and work with ontologies. It is especially apt for machine processing. However, researchers in the field of software evolution have not really taken advantage of that so far. In this paper, we address the potential of representing software evolution knowledge with ontologies and Semantic Web technology, such as Linked Data and automated reasoning. We present Seon, a pyramid of ontologies for software evolution, which describes stakeholders, their activities, artifacts they create, and the relations among all of them. We show the use of evolution-specific ontologies for establishing a shared taxonomy of software analysis services, for defining extensible meta-models, for explicitly describing relationships among artifacts, and for linking data such as code structures, issues (change requests), bugs, and basically any changes made to a system over time. For validation, we discuss three different approaches, which are backed by Seon and enable semantically enriched software evolution analysis. These techniques have been fully implemented as tools and cover software analysis with web services, a natural language query interface for developers, and large-scale software visualization.

The replication of studies in mining software repositories (MSR) is essential to compare different mining techniques or assess their findings across many projects. However, it has been shown that very few of these studies can be easily replicated. Their replication is just as fundamental as the studies themselves and is one of the main threats to validity that they suffer from. In this paper, we show how we can alleviate this problem with our SOFAS framework. SOFAS is a platform that enables a systematic and repeatable analysis of software projects by providing extensible and composable analysis workflows. These workflows can be applied on a multitude of software projects, facilitating the replication and scaling of mining studies. In this paper, we show how and to which degree replication can be achieved. We investigated the mining studies of MSR from 2004 to 2011 and found that from 88 studies published in the MSR proceedings so far, we can fully replicate 25 empirical studies. Additionally, we can replicate 27 additional studies to a large extent. These studies account for 30% and 32%, respectively, of the mining studies published. To support our claim we describe in detail one large study that we replicated and discuss how replication with SOFAS works for the other studies investigated. To discuss the potential of our platform we also characterise how studies can be easily enriched to deliver even more comprehensive answers by extending the analysis workflows provided by the platform.