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.

Historical data about software projects is stored in repositories such as version control, bug tracking and mailing lists. Analyzing such data is vital to discover unthought-of-yet-interesting insights of a software project. Even though a wide range of software analysis techniques are already available, integration of such analyses is yet to be systematically addressed. Inspired from the recently introduced concept of Software as a Service, our research group investigated the concept of Software Analysis as a Service (SOFAS), a distributed and collaborative software analysis platform. SOFAS allows software analyses to be accessed, composed into workflows, and executed over the Internet. However, traditional service composition is a complex, time consuming and error-prone process, which requires experts in both composition languages and existing standards. In this paper, we propose a mashup platform to address the problem of software analysis composition in a light-weight, programming-free process-centric way. Our proposed mashup platform provides design-time guidance to the users throughout the mashup design by integrating a continuous feedback mechanism. It requires exploiting semantic web technologies and Software Engineering Ontologies (SEON).

The concept of Software as a Service has been recently introduced, allowing software to be accessed, composed into workflows, and executed over the Internet. However, there is no guarantee on the execution reliability of such workflows. In case of component service failure, that particular service has to be replaced quickly to ensure the correct execution of the composite service. While most of the existing replacement approaches require the faulty workflow to be completely re-executed, we advocate that dependencies among component services is vitally important for service replacement. In this paper, we present a dependency-based component service recommendation approach that takes into account the dependencies among the component services, such as input/output, pre and post conditions, and correlation among services. Correlation analysis is a new dimension that we introduce to the service replacement algorithms. To demonstrate our approach, we use an existing software analysis services framework as a case study. Finally, we argue that dependencies among services can affect the accuracy of the replacement.

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.

We propose a distributed and collaborative software analysis platform to enable seamless interoperability of software analysis tools across platform, geographical and organizational boundaries. In particular, we devise software analysis tools as services that can be accessed and composed over the Internet. These distributed services shall be widely accessible through a software analysis broker where organizations and research groups can register and share their tools. To enable (semi)-automatic use and composition of these tools, they will be classified and mapped into a software analysis taxonomy and adhere to specific meta-models and ontologies for their category of analysis. We claim that moving software analysis "outside the lab and into the Web" is highly beneficial from many point of views. Simple, common analyses can be effortlessly combined together into much meaningful, complex and novel ones. Analyses can be run everywhere and anytime without the need to install several tools and to cope with many output formats. Empirical studies can be easily replicated. At last, we claim that this will greatly help in the maturing of the field and boost its role in supporting software development practices

The 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.