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Issues related to the dereferencing of null pointers are a pervasive and widely studied problem, and numerous static analyses have been proposed for this purpose. These are typically based on dataflow analysis, and take advantage of annotations indicating whether a type is nullable or not. The presence of such annotations can significantly improve the accuracy of null checkers. However, most code found in the wild is not annotated, and tools must fall back on default assumptions, leading to both false positives and false negatives. Manually annotating code is a laborious task and requires deep knowledge of how a program interacts with clients and components. We propose to infer nullable annotations from an analysis of existing test cases. For this purpose, we execute instrumented tests and capture nullable API interactions. Those recorded interactions are then refined (santitised and propagated) in order to improve their precision and recall. We evaluate our approach on seven projects from the spring ecosystems and two google projects which have been extensively manually annotated with thousands of @Nullable annotations. We find that our approach has a high precision, and can find around half of the existing @Nullable annotations. This suggests that the method proposed is useful to mechanise a significant part of the very labour-intensive annotation task.

This artefact contains a dataset of open-source programs obtained from the Maven Central Repository and scripts that first extract contracts from these programs and then perform several analyses on the contracts extracted. The extraction and analysis is fully automated and directly produces the tables presented in the accompanying paper. The results show how contracts are used in real-world program, and how their usage changes between versions and within inheritance hierarchies.

The use of formal contracts has long been advocated as an approach to develop programs that are provably correct. However, the reality is that adoption of contracts has been slow in practice. Despite this, the adoption of lightweight contracts — typically utilising runtime checking — has progressed. In the case of Java, built-in features of the language (e.g. assertions and exceptions) can be used for this. Furthermore, a number of libraries which facilitate contract checking have arisen. In this paper, we catalogue 25 techniques and tools for lightweight contract checking in Java, and present the results of an empirical study looking at a dataset extracted from the 200 most popular projects found on Maven Central, constituting roughly 351,034 KLOC. We examine (1) the extent to which contracts are used and (2) what kind of contracts are used. We then investigate how contracts are used to safeguard code, and study problems in the context of two types of substitutability that can be guarded by contracts: (3) unsafe evolution of APIs that may break client programs and (4) violations of Liskovs Substitution Principle (LSP) when methods are overridden. We find that: (1) a wide range of techniques and constructs are used to represent contracts, and often the same program uses different techniques at the same time; (2) overall, contracts are used less than expected, with significant differences between programs; (3) projects that use contracts continue to do so, and expand the use of contracts as they grow and evolve; and, (4) there are cases where the use of contracts points to unsafe subtyping (violations of Liskov's Substitution Principle) and unsafe evolution.

This artefact contains a dataset of open-source programs obtained from the Maven Central Repository and scripts that first extract contracts from these programs and then perform several analyses on the contracts extracted. The extraction and analysis is fully automated and directly produces the tables presented in the accompanying paper. The results show how contracts are used in real-world program, and how their usage changes between versions and within inheritance hierarchies.