Previous work has shown that early resolution of issues detected by static code analyzers can prevent major costs later on. However, developers often ignore such issues for two main reasons. First, many issues should be interpreted to determine if they correspond to actual flaws in the program. Second, static analyzers often do not present the issues in a way that is actionable. To address these problems, we present Sorald: a novel system that uses metaprogramming templates to transform the abstract syntax trees of programs and suggests fixes for static analysis warnings. Thus, the burden on the developer is reduced from interpreting and fixing static issues, to inspecting and approving full fledged solutions. Sorald fixes violations of 10 rules from SonarJava, one of the most widely used static analyzers for Java. We evaluate Sorald on a dataset of 161 popular repositories on Github. Our analysis shows the effectiveness of Sorald as it fixes 65% (852/1,307) of the violations that meets the repair preconditions. Overall, our experiments show it is possible to automatically fix notable violations of the static analysis rules produced by the state-of-the-art static analyzer SonarJava.

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.

JSON is an essential file and data format in domains that span scientific computing, web APIs or configuration management. Its popularity has motivated significant software development effort to build multiple libraries to process JSON data. Previous studies focus on performance comparison among these libraries and lack a software engineering perspective. We present the first systematic analysis and comparison of the input / output behavior of 20 JSON libraries, in a single software ecosystem: Java/Maven. We assess behavior diversity by running each library against a curated set of 473 JSON files, including both well-formed and ill-formed files. The main design differences, which influence the behavior of the libraries, relate to the choice of data structure to represent JSON objects and to the encoding of numbers. We observe a remarkable behavioral diversity with ill-formed files, or corner cases such as large numbers or duplicate data. Our unique behavioral assessment of JSON libraries paves the way for a robust processing of ill-formed files, through a multi-version architecture.

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.

Automated diversity is a promising mean of increasing the security of software systems. However, current automated diversity techniques operate at the bottom of the software stack (operating system and compiler), yielding a limited amount of diversity. We present a novel Model-Driven Engineering approach to the diversification of communicating systems, building on abstraction, model transformations and code generation. This approach generates significant amounts of diversity with a low overhead, and addresses a large number of communicating systems, including small communicating devices.

Code{strata} is an interdisciplinary collaboration between art studies researchers (Rennes 2) and computer scientists (INRIA, KTH). It is a sound installation: a computer system unit made of concrete that sits on a wooden desk. The purpose of this project is to question the opacity and simplicity of high-level interfaces used in daily gestures. It takes the form of a 3-D sonification of a full software trace that is collected when performing a copy and paste command in a simple text editor. The user may hear, through headphones, a poetic interpretation of what happens in a computer, behind the of graphical interfaces. The sentence 'Copy and paste' is played back in as many pieces as there are nested functions called during the execution of the command.