As all software, blockchain nodes are exposed to faults in their underlying execution stack. Unstable execution environments can disrupt the availability of blockchain nodes’ interfaces, resulting in downtime for users. This article introduces the concept of N-Version Blockchain nodes. This new type of node relies on simultaneous execution of different implementations of the same blockchain protocol, in the line of Avizienis’ N-Version programming vision. We design and implement an N-Version blockchain node prototype in the context of Ethereum, calledN-ETH. We show thatN-ETHis able to mitigate the effects of unstable execution environments and significantly enhance availability under environment faults. To simulate unstable execution environments, we perform fault injection at the system-call level. Our results show that existing Ethereum node implementations behave asymmetrically under identical instability scenarios.N-ETHleverages this asymmetric behavior available in the diverse implementations of Ethereum nodes to provide increased availability, even under our most aggressive fault-injection strategies. We are the first to validate the relevance of N-Version design in the domain of blockchain infrastructure. From an industrial perspective, our results are of utmost importance for businesses operating blockchain nodes, including Google, ConsenSys, and many other major blockchain companies.

Software bloat is code that is packaged in an application but is actually not necessary to run the application. The presence of software bloat is an issue for security, performance, and for maintenance. In this article, we introduce a novel technique for debloating, which we call coverage-based debloating. We implement the technique for one single language: Java bytecode. We leverage a combination of state-of-the-art Java bytecode coverage tools to precisely capture what parts of a project and its dependencies are used when running with a specific workload. Then, we automatically remove the parts that are not covered, in order to generate a debloated version of the project. We succeed to debloat 211 library versions from a dataset of 94 unique open-source Java libraries. The debloated versions are syntactically correct and preserve their original behaviour according to the workload. Our results indicate that 68.3% of the libraries’ bytecode and 20.3% of their total dependencies can be removed through coverage-based debloating. For the first time in the literature on software debloating, we assess the utility of debloated libraries with respect to client applications that reuse them. We select 988 client projects that either have a direct reference to the debloated library in their source code or which test suite covers at least one class of the libraries that we debloat. Our results show that 81.5% of the clients, with at least one test that uses the library, successfully compile and pass their test suite when the original library is replaced by its debloated version.

Large-scale code reuse significantly reduces both development costs and time. However, the massive share of third-party code in software projects poses new challenges, especially in terms of maintenance and security. In this paper, we propose a novel technique to specialize dependencies of Java projects, based on their actual usage. Given a project and its dependencies, we systematically identify the subset of each dependency that is necessary to build the project, and we remove the rest. As a result of this process, we package each specialized dependency in aJARfile. Then, we generate specialized dependency trees where the original dependencies are replaced by the specialized versions. This allows building the project with significantly less third-party code than the original. As a result, the specialized dependencies become a first-class concept in the software supply chain, rather than a transient artifact in an optimizing compiler toolchain. We implement our technique in a tool calledDepTrim, which we evaluate with 30 notable open-source Java projects.DepTrimspecializes a total of 343 (86.6%) dependencies across these projects, and successfully rebuilds each project with a specialized dependency tree. Moreover, through this specialization,DepTrimremoves a total of 57,444 (42.2%) classes from the dependencies, reducing the ratio of dependency classes to project classes from 8.7$\boldsymbol{\times}$in the original projects to 5.0$\boldsymbol{\times}$after specialization. These novel results indicate that dependency specialization significantly reduces the share of third-party code in Java projects.

Software engineering researchers look for software artifacts to study their characteristics or to evaluate new techniques. In this paper, we introduce Duets, a new dataset of software libraries and their clients. This dataset can be exploited to gain many different insights, such as API usage, usage inputs, or novel observations about the test suites of clients and libraries. Duets is meant to support both static and dynamic analysis. This means that the libraries and the clients compile correctly, they are executable and their test suites pass. The dataset is composed of open-source projects that have more than five stars on GitHub. The final dataset contains 395 libraries and 2,874 clients. Additionally, we provide the raw data that we use to create this dataset, such as 34,560 pom.xml files or the complete file list from 34,560 projects. This dataset can be used to study how libraries are used by their clients or as a list of software projects that successfully build. The client's test suite can be used as an additional verification step for code transformation techniques that modify the libraries.

We study the evolution and impact of bloated dependencies in a single software ecosystem: Java/Maven. Bloated dependencies are third-party libraries that are packaged in the application binary but are not needed to run the application. We analyze the history of 435 Java projects. This historical data includes 48,469 distinct dependencies, which we study across a total of 31,515 versions of Maven dependency trees. Bloated dependencies steadily increase over time, and 89.2% of the direct dependencies that are bloated remain bloated in all subsequent versions of the studied projects. This empirical evidence suggests that developers can safely remove a bloated dependency. We further report novel insights regarding the unnecessary maintenance efforts induced by bloat. We find that 22% of dependency updates performed by developers are made on bloated dependencies, and that Dependabot suggests a similar ratio of updates on bloated dependencies.

Abstract Build automation tools and package managers have a profound influence on software development. They facilitate the reuse of third-party libraries, support a clear separation between the application’s code and its external dependencies, and automate several software development tasks. However, the wide adoption of these tools introduces new challenges related to dependency management. In this paper, we propose an original study of one such challenge: the emergence of bloated dependencies. Bloated dependencies are libraries that are packaged with the application’s compiled code but that are actually not necessary to build and run the application. They artificially grow the size of the built binary and increase maintenance effort. We propose DepClean, a tool to determine the presence of bloated dependencies in Maven artifacts. We analyze 9,639 Java artifacts hosted on Maven Central, which include a total of 723,444 dependency relationships. Our key result is as follows: 2.7% of the dependencies directly declared are bloated, 15.4% of the inherited dependencies are bloated, and 57% of the transitive dependencies of the studied artifacts are bloated. In other words, it is feasible to reduce the number of dependencies of Maven artifacts to 1/4 of its current count. Our qualitative assessment with 30 notable open-source projects indicates that developers pay attention to their dependencies when they are notified of the problem. They are willing to remove bloated dependencies: 21/26 answered pull requests were accepted and merged by developers, removing 140 dependencies in total: 75 direct and 65 transitive.

The Maven Central Repository provides an extraordinary source of data to understand complex architecture and evolution phenomena among Java applications. As of September 6, 2018, this repository includes 2.8M artifacts (compiled piece of code implemented in a JVM-based language), each of which is characterized with metadata such as exact version, date of upload and list of dependencies towards other artifacts. Today, one who wants to analyze the complete ecosystem of Maven artifacts and their dependencies faces two key challenges: (i) this is a huge data set; and (ii) dependency relationships among artifacts are not modeled explicitly and cannot be queried. In this paper, we present the Maven Dependency Graph. This open source data set provides two contributions: a snapshot of the whole Maven Central taken on September 6, 2018, stored in a graph database in which we explicitly model all dependencies; an open source infrastructure to query this huge dataset.

Maven artifacts are immutable: an artifact that is uploaded on Maven Central cannot be removed nor modified. The only way for developers to upgrade their library is to release a new version. Consequently, Maven Central accumulates all the versions of all the libraries that are published there, and applications that declare a dependency towards a library can pick any version. In this work, we hypothesize that the immutability of Maven artifacts and the ability to choose any version naturally support the emergence of software diversity within Maven Central. We analyze 1,487,956 artifacts that represent all the versions of 73,653 libraries. We observe that more than 30% of libraries have multiple versions that are actively used by latest artifacts. In the case of popular libraries, more than 50% of their versions are used. We also observe that more than 17% of libraries have several versions that are significantly more used than the other versions. Our results indicate that the immutability of artifacts in Maven Central does support a sustained level of diversity among versions of libraries in the repository.

During compilation from Java source code to bytecode, some information is irreversibly lost. In other words, compilation and decompilation of Java code is not symmetric. Consequently, the decompilation process, which aims at producing source code from bytecode, must establish some strategies to reconstruct the information that has been lost. Modern Java decompilers tend to use distinct strategies to achieve proper decompilation. In this work, we hypothesize that the diverse ways in which bytecode can be decompiled has a direct impact on the quality of the source code produced by decompilers. We study the effectiveness of eight Java decompilers with respect to three quality indicators: syntactic correctness, syntactic distortion and semantic equivalence modulo inputs. This study relies on a benchmark set of 14 real-world open-source software projects to be decompiled (2041 classes in total). Our results show that no single modern decompiler is able to correctly handle the variety of bytecode structures coming from real-world programs. Even the highest ranking decompiler in this study produces syntactically correct output for 84% of classes of our dataset and semantically equivalent code output for 78% of classes.

A growing body of research in empirical software engineering applies recurrent patterns analysis in order to make sense of the developers' behavior during their interactions with IDEs. However, the exploration of hidden real-time structures of programming behavior remains a challenging task. In this paper, we investigate the presence of temporal behavioral patterns (T-patterns) in debugging activities using the THEME software. Our preliminary exploratory results show that debugging activities are strongly correlated with code editing, file handling, window interactions and other general types of programming activities. The validation of our T-patterns detection approach demonstrates that debugging activities are performed on the basis of repetitive and well-organized behavioral events. Furthermore, we identify a large set of T-patterns that associate debugging activities with build success, which corroborates the positive impact of debugging practices on software development.