NDLI: Data-guided repair of selection statements

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Data-guided repair of selection statements

Content Provider	ACM Digital Library
Author	Chandra, Satish Gopinath, Divya Saha, Diptikalyan Khurshid, Sarfraz
Abstract	Database-centric programs form the backbone of many enterprise systems. Fixing defects in such programs takes much human effort due to the interplay between imperative code and database-centric logic. This paper presents a novel data-driven approach for automated fixing of bugs in the selection condition of database statements (e.g., WHERE clause of SELECT statements) – a common form of bugs in such programs. Our key observation is that in real-world data, there is information latent in the distribution of data that can be useful to repair selection conditions efficiently. Given a faulty database program and input data, only a part of which induces the defect, our novelty is in determining the correct behavior for the defect-inducing data by taking advantage of the information revealed by the rest of the data. We accomplish this by employing semi-supervised learning to predict the correct behavior for defect-inducing data and by patching up any inaccuracies in the prediction by a SAT-based combinatorial search. Next, we learn a compact decision tree for the correct behavior, including the correct behavior on the defect-inducing data. This tree suggests a plausible fix to the selection condition. We demonstrate the feasibility of our approach on seven realworld examples. .
Starting Page	243
Ending Page	253
Page Count	11
File Format	PDF
ISBN	9781450327565
DOI	10.1145/2568225.2568303
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-05-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Support vector machines Program repair Databases Machine learning Sat Data-centric programs Abap
Content Type	Text
Resource Type	Article

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