{"paper":{"title":"Implementing CPSLint: A Data Validation and Sanitisation Tool for Industrial Cyber-Physical Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"CPSLint is a domain-specific language that expresses industrial CPS data sanitization and validation in just a few lines of code.","cross_cats":[],"primary_cat":"cs.PL","authors_text":"Mari\\\"elle Stoelinga, \\\"Omer Sayilir, Uraz Odyurt, Vadim Zaytsev","submitted_at":"2026-04-20T12:46:13Z","abstract_excerpt":"Raw datasets are often too large and unstructured to work with directly, and require a data preparation phase. The domain of industrial Cyber-Physical Systems (CPSs) is no exception, as raw data typically consists of large time-series data collections that log the system's status at regular time intervals. The processing of such raw data is often carried out using ad hoc, case-specific, one-off Python scripts, often neglecting aspects of readability, reusability, and maintainability. In practice, this can cause professionals such as data scientists to write similar data preparation scripts for"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We introduce CPSLint, a Domain-Specific Language (DSL) designed to support the data preparation process for industrial CPS. ... In our DSL one can express the data preparation process in just a few lines of code. CPSLint is a publicly available tool applicable for any case involving time-series data collections in need of sanitisation.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"We leverage the fact that many raw data collections in the industrial CPS domain require similar actions to render them suitable for data-centric workflows.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"CPSLint is a publicly available DSL that lets users express data sanitization for CPS time-series collections in a few lines of code, improving readability and maintainability over custom scripts.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"CPSLint is a domain-specific language that expresses industrial CPS data sanitization and validation in just a few lines of code.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"b1f26f309ffac2d41825e2b75a761110277059d07e0e2764e45a92e45e523a87"},"source":{"id":"2604.18191","kind":"arxiv","version":2},"verdict":{"id":"40ceaa2f-37d4-4d5c-84d3-6910efb6b64d","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T03:30:15.057428Z","strongest_claim":"We introduce CPSLint, a Domain-Specific Language (DSL) designed to support the data preparation process for industrial CPS. ... In our DSL one can express the data preparation process in just a few lines of code. CPSLint is a publicly available tool applicable for any case involving time-series data collections in need of sanitisation.","one_line_summary":"CPSLint is a publicly available DSL that lets users express data sanitization for CPS time-series collections in a few lines of code, improving readability and maintainability over custom scripts.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"We leverage the fact that many raw data collections in the industrial CPS domain require similar actions to render them suitable for data-centric workflows.","pith_extraction_headline":"CPSLint is a domain-specific language that expresses industrial CPS data sanitization and validation in just a few lines of code."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.18191/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-20T04:21:15.577126Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"8e290f6be16df63b0bd8064039db5248dc149dfd3d706e9fc9e35019ca478d24"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}