Blogs (1) >>
ICSE 2019
Sat 25 - Fri 31 May 2019 Montreal, QC, Canada
Wed 29 May 2019 12:00 - 12:10 at Laurier - Automated Program Repair 1 Chair(s): Lars Grunske

Several static analysis tools, such as Splint or FindBugs, have been proposed to the software development community to help detect security vulnerabilities or bad programming practices. However, the adoption of these tools is hindered by their high false positive rates. If the false positive rate is too high, developers may get acclimated to violation reports from these tools, causing concrete and severe bugs being overlooked. Fortunately, some violations are actually addressed and resolved by developers. We claim that those violations that are recurrently fixed are likely to be true positives, and an automated approach can learn to repair similar unseen violations. However, there is lack of a systematic way to investigate the distributions on existing violations and fixed ones in the wild,that can provide insights into prioritizing violations for developers, and an effective way to mine code and fix patterns which can help developers easily understand the reasons of leading violations and how to fix them. In this paper, we first collect and track a large number of fixed and unfixed violations across revisions of software. The empirical analyses reveal that there are discrepancies in the distributions of violations that are detected and those that are fixed, in terms of occurrences, spread and categories, which can provide insights into prioritizing violations. To automatically identify patterns in violations and their fixes, we propose an approach that utilizes convolutional neural networks to learn features and clustering to regroup similar instances. We then evaluate the usefulness of the identified fix patterns by applying them to unfixed violations. The results show that developers will accept and merge a majority (69/116) of fixes generated from the inferred fix patterns. It is also noteworthy that the yielded patterns are applicable to four real bugs in the Defects4J major benchmark for software testing and automated repair.

Wed 29 May
Times are displayed in time zone: Eastern Time (US & Canada) change

11:00 - 12:30: Automated Program Repair 1Papers / Journal-First Papers / Technical Track at Laurier
Chair(s): Lars GrunskeHumboldt-Universität zu Berlin
11:00 - 11:20
Talk
Technical Track
Kui LiuUniversity of Luxembourg, Dongsun KimFuriosa.ai, Tegawendé F. BissyandéSnT, University of Luxembourg, Taeyoung KimChonbuk National University, Kisub KimUniversity of Luxembourg, SnT, Anil KoyuncuUniversity of Luxembourg, Luxembourg, Suntae Kim, Yves Le TraonUniversity of Luxembourg
Pre-print
11:20 - 11:40
Talk
Technical Track
Seemanta SahaUniversity of California Santa Barbara, Ripon SahaFujitsu Laboratories of America, Inc., Mukul PrasadFujitsu Laboratories of America
11:40 - 12:00
Talk
Technical Track
Michele TufanoCollege of William and Mary, Jevgenija PantiuchinaUniversità della Svizzera italiana, Cody Watson , Gabriele BavotaUniversità della Svizzera italiana (USI), Denys PoshyvanykWilliam and Mary
Pre-print
12:00 - 12:10
Talk
Journal-First Papers
Kui LiuUniversity of Luxembourg, Dongsun KimFuriosa.ai, Tegawendé F. BissyandéSnT, University of Luxembourg, Shin YooKorea Advanced Institute of Science and Technology, Yves Le TraonUniversity of Luxembourg
Pre-print
12:10 - 12:20
Talk
Journal-First Papers
Sergey MechtaevUniversity College London, Xiang GaoNational University of Singapore, Shin Hwei TanSouthern University of Science and Technology, Abhik RoychoudhuryNational University of Singapore
12:20 - 12:30
Talk
Papers