The Best of Both Worlds: Combining Learned Embeddings with Engineered Features for Accurate Prediction of Correct Patches

in ACM Transactions on Software Engineering and Methodology (2022)

Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness

in Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness (2022)

Finding Data Compatibility Bugs with JSON Subschema Checking

in The 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) (2021, July 11)

JSON is a data format used pervasively in web APIs, cloud computing, NoSQL databases, and increasingly also machine learning. To ensure that JSON data is compatible with an application, one can define a ... [more ▼]

JSON is a data format used pervasively in web APIs, cloud computing, NoSQL databases, and increasingly also machine learning. To ensure that JSON data is compatible with an application, one can define a JSON schema and use a validator to check data against the schema. However, because validation can happen only once concrete data occurs during an execution, it may detect data compatibility bugs too late or not at all. Examples include evolving the schema for a web API, which may unexpectedly break client applications, or accidentally running a machine learning pipeline on incorrect data. This paper presents a novel way of detecting a class of data compatibility bugs via JSON subschema checking. Subschema checks find bugs before concrete JSON data is available and across all possible data specified by a schema. For example, one can check if evolving a schema would break API clients or if two components of a machine learning pipeline have incompatible expectations about data. Deciding whether one JSON schema is a subschema of another is non-trivial because the JSON Schema specification language is rich. Our key insight to address this challenge is to first reduce the richness of schemas by canonicalizing and simplifying them, and to then reason about the subschema question on simpler schema fragments using type-specific checkers. We apply our subschema checker to thousands of real-world schemas from different domains. In all experiments, the approach is correct whenever it gives an answer (100% precision and correctness), which is the case for most schema pairs (93.5% recall), clearly outperforming the state-of-the-art tool. Moreover, the approach reveals 43 previously unknown bugs in popular software, most of which have already been fixed, showing that JSON subschema checking helps finding data compatibility bugs early. [less ▲]