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{{short description|Automatic repair of software bugs}}
'''Automatic bug-fixing''' is the automatic [[Patch (computing)|repair]] of [[software bug]]s without the intervention of a human programmer.<ref>{{Cite journal |last=Rinard |first=Martin C. |year=2008 |title=Technical perspective ''Patching'' program errors |journal=Communications of the ACM |volume=51 |issue=12 |pages=86 |doi=10.1145/1409360.1409381 |s2cid=28629846}}</ref><ref>{{Cite journal |last=Harman |first=Mark |year=2010 |title=Automated patching techniques |journal=Communications of the ACM |volume=53 |issue=5 |pages=108 |doi=10.1145/1735223.1735248 |s2cid=9729944}}</ref><ref name="Gazzola2019">{{Cite journal |last1=Gazzola |first1=Luca |last2=Micucci |first2=Daniela |last3=Mariani |first3=Leonardo |year=2019 |title=Automatic Software Repair: A Survey |url=https://boa.unimib.it/bitstream/10281/184798/2/08089448_final.pdf |journal=IEEE Transactions on Software Engineering |volume=45 |issue=1 |pages=34–67 |doi=10.1109/TSE.2017.2755013 |hdl=10281/184798 |s2cid=57764123|doi-access=free }}</ref> It is also commonly referred to as ''automatic patch generation'', ''automatic bug repair'', or ''automatic program repair''.<ref name="Gazzola2019">{{Cite journal |last1=Gazzola |first1=Luca |last2=Micucci |first2=Daniela |last3=Mariani |first3=Leonardo |year=2019 |title=Automatic Software Repair: A Survey |url=https://boa.unimib.it/bitstream/10281/184798/2/08089448_final.pdf |journal=IEEE Transactions on Software Engineering |volume=45 |issue=1 |pages=34–67 |doi=10.1109/TSE.2017.2755013 |hdl=10281/184798 |s2cid=57764123|doi-access=free }}</ref> The typical goal of such techniques is to automatically generate correct [[Patch (computing)|patches]] to eliminate bugs in [[software program]]s without causing [[software regression]].<ref>{{Cite book |last1=Tan |first1=Shin Hwei |title=2015 IEEE/ACM 37th IEEE International Conference on Software Engineering |last2=Roychoudhury |first2=Abhik |date=2015 |publisher=IEEE |isbn=978-1-4799-1934-5 |pages=471–482 |chapter=relifix: Automated repair of software regressions |doi=10.1109/ICSE.2015.65 |s2cid=17125466}}</ref>
==
Automatic bug fixing is made according to a specification of the expected behavior which can be for instance a [[formal specification]] or a [[test suite]].<ref name="genprog2009">{{Cite book |last1=Weimer |first1=Westley |title=Proceedings of the 31st International Conference on Software Engineering |last2=Nguyen |first2=ThanhVu |last3=Le Goues |first3=Claire|author3-link=Claire Le Goues |last4=Forrest |first4=Stephanie |date=2009 |publisher=IEEE |isbn=978-1-4244-3453-4 |pages=364–374 |chapter=Automatically finding patches using genetic programming |citeseerx=10.1.1.147.8995 |doi=10.1109/ICSE.2009.5070536 |s2cid=1706697}}</ref>
A test-suite – the input/output pairs specify the functionality of the program, possibly captured in [[Assertion (software development)|assertions]] can be used as a [[test oracle]] to drive the search. This oracle can in fact be divided between the ''bug oracle'' that exposes the faulty behavior, and the ''regression oracle'', which encapsulates the functionality any program repair method must preserve. Note that a test suite is typically incomplete and does not cover all possible cases. Therefore, it is often possible for a validated patch to produce expected outputs for all inputs in the test suite but incorrect outputs for other inputs.<ref name="kali">{{Cite book |last1=Qi |first1=Zichao |title=Proceedings of the 2015 International Symposium on Software Testing and Analysis |last2=Long |first2=Fan |last3=Achour |first3=Sara |last4=Rinard |first4=Martin |date=2015 |publisher=ACM |isbn=978-1-4503-3620-8 |chapter=An Analysis of Patch Plausibility and Correctness for Generate-and-Validate Patch Generation Systems |citeseerx=10.1.1.696.5616 |doi=10.1145/2771783.2771791 |s2cid=6845282}}</ref> The existence of such validated but incorrect patches is a major challenge for generate-and-validate techniques.<ref name="kali" /> Recent successful automatic bug-fixing techniques often rely on additional information other than the test suite, such as information learned from previous human patches, to further identify correct patches among validated patches.<ref name="prophet">{{Cite book |last1=Long |first1=Fan |title=Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages |last2=Rinard |first2=Martin |date=2016 |publisher=ACM |isbn=978-1-4503-3549-2 |pages=298–312 |chapter=Automatic patch generation by learning correct code |doi=10.1145/2837614.2837617 |s2cid=6091588}}</ref>
Another way to specify the expected behavior is to use [[formal specification]]s<ref name="autofixe">{{Cite journal |last1=Pei |first1=Yu |last2=Furia |first2=Carlo A. |last3=Nordio |first3=Martin |last4=Wei |first4=Yi |last5=Meyer |first5=Bertrand |last6=Zeller |first6=Andreas |date=May 2014 |title=Automated Fixing of Programs with Contracts |journal=IEEE Transactions on Software Engineering |volume=40 |issue=5 |pages=427–449 |arxiv=1403.1117 |bibcode=2014arXiv1403.1117P |doi=10.1109/TSE.2014.2312918 |s2cid=53302638}}</ref><ref>{{Cite journal |title=Contract-based Data Structure Repair Using Alloy |citeseerx=10.1.1.182.4390}}</ref> Verification against full specifications that specify the whole program behavior including functionalities is less common because such specifications are typically not available in practice and the computation cost of such [[formal verification|verification]] is prohibitive. For specific classes of errors, however, implicit partial specifications are often available. For example, there are targeted bug-fixing techniques validating that the patched program can no longer trigger overflow errors in the same execution path.<ref name="codephage" />
== Techniques ==
=== Generate-and-validate ===
Generate-and-validate approaches compile and test each candidate patch to collect all validated patches that produce expected outputs for all inputs in the test suite.<ref name="genprog2009" /><ref name="kali" /> Such a technique typically starts with a test suite of the program, i.e., a set of [[test case (software)|test case]]s, at least one of which exposes the bug.<ref name="genprog2009" /><ref name="prophet" /><ref name="rsrepair">{{Cite book |last1=Qi |first1=Yuhua |title=Proceedings of the 36th International Conference on Software Engineering |last2=Mao |first2=Xiaoguang |last3=Lei |first3=Yan |last4=Dai |first4=Ziying |last5=Wang |first5=Chengsong |date=2014 |publisher=ACM |isbn=978-1-4503-2756-5 |series=ICSE 2014 |___location=Austin, Texas |pages=254–265 |chapter=The Strength of Random Search on Automated Program Repair |doi=10.1145/2568225.2568254 |s2cid=14976851}}</ref><ref name="spr">{{Cite book |last1=Long |first1=Fan |title=Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering |last2=Rinard |first2=Martin |date=2015 |publisher=ACM |isbn=978-1-4503-3675-8 |series=ESEC/FSE 2015 |___location=Bergamo, Italy |pages=166–178 |chapter=Staged Program Repair with Condition Synthesis |citeseerx=10.1.1.696.9059 |doi=10.1145/2786805.2786811 |s2cid=5987616}}</ref> An early generate-and-validate bug-fixing systems is GenProg.<ref name="genprog2009" /> The effectiveness of generate-and-validate techniques remains controversial, because they typically do not provide [[#Limitations of automatic bug-fixing|patch correctness guarantees]].<ref name="kali" /> Nevertheless, the reported results of recent state-of-the-art techniques are generally promising. For example, on systematically collected 69 real world bugs in eight large [[C (programming language)|C software programs]], the state-of-the-art bug-fixing system Prophet generates correct patches for 18 out of the 69 bugs.<ref name="prophet" />
<!-- mutation based repair -->
One way to generate candidate patches is to apply [[program mutation|mutation operators]] on the original program. Mutation operators manipulate the original program, potentially via its [[abstract syntax tree]] representation, or a more coarse-grained representation such as operating at the [[Statement (programming)|statement]]-level or [[Block (programming)|block]]-level. Earlier [[Genetic improvement (computer science)|genetic improvement]] approaches operate at the statement level and carry out simple delete/replace operations such as deleting an existing statement or replacing an existing statement with another statement in the same source file.<ref name=genprog2009 /><ref name="genprog2012">{{Cite book |last1=Le Goues |first1=Claire|author1-link=Claire Le Goues |title=2012 34th International Conference on Software Engineering (ICSE) |last2=Dewey-Vogt |first2=Michael |last3=Forrest |first3=Stephanie |last4=Weimer |first4=Westley |date=2012 |publisher=IEEE |isbn=978-1-4673-1067-3 |pages=3–13 |chapter=A Systematic Study of Automated Program Repair: Fixing 55 out of 105 Bugs for $8 Each |citeseerx=10.1.1.661.9690 |doi=10.1109/ICSE.2012.6227211 |s2cid=10987936}}</ref> Recent approaches use more fine-grained operators at the [[abstract syntax tree]] level to generate more diverse set of candidate patches.<ref name=spr /> Notably, the statement deletion mutation operator, and more generally removing code, is a reasonable repair strategy, or at least a good fault localization strategy.<ref>{{Cite book |last1=Qi |first1=Zichao |last2=Long |first2=Fan |last3=Achour |first3=Sara |last4=Rinard |first4=Martin |title=Proceedings of the 2015 International Symposium on Software Testing and Analysis |chapter=An analysis of patch plausibility and correctness for generate-and-validate patch generation systems |date=2015-07-13 |chapter-url=http://dx.doi.org/10.1145/2771783.2771791 |pages=24–36 |___location=New York, NY, USA |publisher=ACM |doi=10.1145/2771783.2771791|hdl=1721.1/101586 |isbn=9781450336208 |s2cid=6845282 }}</ref>
<!-- fix templates -->
Another way to generate candidate patches consists of using fix templates. Fix templates are typically predefined changes for fixing specific classes of bugs.<ref name=par /> Examples of fix templates include inserting a [[Conditional (computer programming)|conditional statement]] to check whether the value of a variable is null to fix null pointer exception, or changing an integer constant by one to fix off-by-one errors.<ref name="par" />
===
Repair techniques exist that are based on symbolic execution. For example, Semfix<ref name="semfix">{{Cite book |last1=Nguyen |first1=Hoang Duong Thien |title=Proceedings of the 2013 International Conference on Software Engineering |last2=Qi |first2=Dawei |last3=Roychoudhury |first3=Abhik |last4=Chandra |first4=Satish |date=2013 |publisher=IEEE Press |isbn=978-1-4673-3076-3 |series=ICSE '13' |___location=San Francisco, California |pages=772–781 |chapter=SemFix: Program Repair via Semantic Analysis}}</ref> uses symbolic execution to extract a repair constraint. Angelix<ref name="angelix">{{Cite book |last1=Mechtaev |first1=Sergey |title=Proceedings of the 38th International Conference on Software Engineering, ICSE 2016, Austin, Texas, May 14-22, 2016 |last2=Yi |first2=Jooyong |last3=Roychoudhury |first3=Abhik |date=2016 |pages=691–701 |chapter=Angelix: scalable multiline program patch synthesis via symbolic analysis}}</ref> introduced the concept of angelic forest in order to deal with multiline patches.
<!-- repair and synthesis -->
Under certain assumptions, it is possible to state the repair problem as a synthesis problem.
SemFix<ref name="semfix" /> uses component-based synthesis.<ref>{{Cite book |last1=Jha |first1=Susmit |url=http://techreports.lib.berkeley.edu/accessPages/EECS-2010-15.html |title=Oracle-guided component-based program synthesis |last2=Gulwani |first2=Sumit |last3=Seshia |first3=Sanjit A. |last4=Tiwari |first4=Ashish |date=2010-05-01 |publisher=ACM |isbn=9781605587196 |pages=215–224 |doi=10.1145/1806799.1806833 |s2cid=6344783}}</ref>
Dynamoth uses dynamic synthesis.<ref>{{Cite book |last1=Galenson |first1=Joel |title=CodeHint: dynamic and interactive synthesis of code snippets |last2=Reames |first2=Philip |last3=Bodik |first3=Rastislav |last4=Hartmann |first4=Björn |last5=Sen |first5=Koushik |date=2014-05-31 |publisher=ACM |isbn=9781450327565 |pages=653–663 |doi=10.1145/2568225.2568250 |s2cid=10656182}}</ref>
S3<ref>{{Cite book |last1=Le |first1=Xuan-Bach D. |url=https://ink.library.smu.edu.sg/sis_research/3917 |title=Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering - ESEC/FSE 2017 |last2=Chu |first2=Duc-Hiep |last3=Lo |first3=David |last4=Le Goues |first4=Claire |author4-link=Claire Le Goues|last5=Visser |first5=Willem |date=2017-08-21 |publisher=ACM |isbn=9781450351058 |pages=593–604 |doi=10.1145/3106237.3106309 |s2cid=1503790}}</ref> is based on [[syntax-guided synthesis]].<ref>{{Cite book |last1=Alur |first1=Rajeev |title=2013 Formal Methods in Computer-Aided Design |last2=Bodik |first2=Rastislav |last3=Juniwal |first3=Garvit |last4=Martin |first4=Milo M. K. |last5=Raghothaman |first5=Mukund |last6=Seshia |first6=Sanjit A. |last7=Singh |first7=Rishabh |last8=Solar-Lezama |first8=Armando |last9=Torlak |first9=Emina |author9-link=Emina Torlak|year=2013 |isbn=9780983567837 |pages=1–8 |chapter=Syntax-guided synthesis |citeseerx=10.1.1.377.2829 |doi=10.1109/fmcad.2013.6679385 |last10=Udupa |first10=Abhishek}}</ref>
SearchRepair<ref name="searchrepair">{{Cite book |last1=Ke |first1=Yalin |title=Proceedings of the 2015 30th IEEE/ACM International Conference on Automated Software Engineering |last2=Stolee |first2=Kathryn |last3=Le Goues |first3=Claire |author3-link=Claire Le Goues|last4=Brun |first4=Yuriy |date=2015 |publisher=ACM |isbn=978-1-5090-0025-8 |series=ASE 2015 |___location=Lincoln, Nebraska |pages=295–306 |chapter=Repairing Programs with Semantic Code Search |doi=10.1109/ASE.2015.60 |s2cid=16361458}}</ref> converts potential patches into an SMT formula and queries candidate patches that allow the patched program to pass all supplied test cases.
=== Data-driven ===
[[Machine learning]] techniques can improve the effectiveness of automatic bug-fixing systems.<ref name="prophet" /> One example of such techniques learns from past successful patches from human developers collected from [[open-source software|open source]] [[software repository|repositories]] in [[GitHub]] and [[SourceForge]].<ref name="prophet" /> It then use the learned information to recognize and prioritize potentially correct patches among all generated candidate patches.<ref name="prophet" /> Alternatively, patches can be directly mined from existing sources. Example approaches include mining patches from donor applications<ref name="codephage" /> or from QA web sites.<ref name="QAFix" />
Getafix<ref name=":0">{{Cite journal |last1=Bader |first1=Johannes |last2=Scott |first2=Andrew |last3=Pradel |first3=Michael |last4=Chandra |first4=Satish |date=2019-10-10 |title=Getafix: learning to fix bugs automatically |journal=Proceedings of the ACM on Programming Languages |volume=3 |issue=OOPSLA |pages=159:1–159:27 |doi=10.1145/3360585|doi-access=free |arxiv=1902.06111 }}</ref> is a language-agnostic approach developed and used in production at [[Facebook, Inc.|Facebook]]. Given a sample of [[Commit (version control)|code commits]] where engineers fixed a certain kind of bug, it learns human-like fix patterns that apply to future bugs of the same kind. Besides using Facebook's own [[Repository (version control)|code repositories]] as training data, Getafix learnt some fixes from [[open source]] Java repositories. When new bugs get detected, Getafix applies its previously learnt patterns to produce candidate fixes and ranks them within seconds. It presents only the top-ranked fix for final validation by tools or an engineer, in order to save resources and ideally be so fast that no human time was spent on fixing the same bug, yet.
===
For specific classes of errors, targeted automatic bug-fixing techniques use specialized templates:
* [[null pointer exception]] repair<ref name="rcv">{{Cite book |last1=Long |first1=Fan |title=Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation |last2=Sidiroglou-Douskos |first2=Stelios |last3=Rinard |first3=Martin |date=2014 |publisher=ACM |isbn=978-1-4503-2784-8 |series=PLDI '14' |___location=New York, New York |pages=227–238 |chapter=Automatic Runtime Error Repair and Containment via Recovery Shepherding |doi=10.1145/2594291.2594337 |s2cid=6252501}}</ref><ref name="nullfix">{{Cite book |last1=Dobolyi |first1=Kinga |title=2008 19th International Symposium on Software Reliability Engineering (ISSRE) |last2=Weimer |first2=Westley |year=2008 |pages=47–56 |chapter=Changing Java's Semantics for Handling Null Pointer Exceptions |citeseerx=10.1.1.147.6158 |doi=10.1109/ISSRE.2008.59 |s2cid=1454939}}</ref><ref name="par">{{Cite book |last1=Kim |first1=Dongsun |title=Proceedings of the 2013 International Conference on Software Engineering |last2=Nam |first2=Jaechang |last3=Song |first3=Jaewoo |last4=Kim |first4=Sunghun |date=2013 |publisher=IEEE Press |isbn=978-1-4673-3076-3 |series=ICSE '13' |pages=802–811 |chapter=Automatic Patch Generation Learned from Human-written Patches}}</ref> with insertion of a [[Conditional (computer programming)|conditional statement]] to check whether the value of a variable is null.
* [[integer overflow]] repair<ref name="codephage">{{Cite book |last1=Sidiroglou |first1=Stelios |title=Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation |last2=Lahtinen |first2=Eric |last3=Long |first3=Fan |last4=Rinard |first4=Martin |date=2015 |chapter=Automatic Error Elimination by Multi-Application Code Transfer}}</ref>
* [[buffer overflow]] repair<ref name="codephage" />
* [[memory leak]] repair,<ref name="leakfix">{{Cite book |last1=Gao |first1=Qing |title=Proceedings of the 37th International Conference on Software Engineering – Volume 1 |last2=Xiong |first2=Yingfei |last3=Mi |first3=Yaqing |last4=Zhang |first4=Lu |last5=Yang |first5=Weikun |last6=Zhou |first6=Zhaoping |last7=Xie |first7=Bing |last8=Mei |first8=Hong |date=2015 |publisher=IEEE Press |isbn=978-1-4799-1934-5 |series=ICSE '15' |___location=Piscataway, New Jersey |pages=459–470 |chapter=Safe Memory-leak Fixing for C Programs}}</ref> with automated insertion of missing memory deallocation statements.
Comparing to generate-and-validate techniques, template-based techniques tend to have better bug-fixing accuracy but a much narrowed scope.<ref name="kali" /><ref name="leakfix" />
== Use ==
There are multiple uses of automatic bug fixing:
* In a development environment: When encountering a bug the developer activates a feature to search for a patch (for instance by clicking on a button). This search can also happen in the background, when the IDE proactively searches for solutions to potential problems, without waiting for explicit action from the developer.<ref>{{Cite journal |last1=Muşlu |first1=Kıvanç |last2=Brun |first2=Yuriy |last3=Holmes |first3=Reid |last4=Ernst |first4=Michael D. |last5=Notkin |first5=David |last6=Muşlu |first6=Kıvanç |last7=Brun |first7=Yuriy |last8=Holmes |first8=Reid |last9=Ernst |first9=Michael D. |last10=Notkin |first10=David |date=19 October 2012 |title=Speculative analysis of integrated development environment recommendations, Speculative analysis of integrated development environment recommendations |journal=ACM SIGPLAN Notices |volume=47 |issue=10 |pages=669, 669–682, 682 |citeseerx=10.1.1.259.6341 |doi=10.1145/2384616.2384665 |issn=0362-1340 |s2cid=5795141}}</ref>
* At runtime: When a failure happens at runtime, a binary patch can be searched for and [[Self-modifying code|applied online]]. An example of such a repair system is ClearView,<ref name="clearview">{{Cite book |last=Perkins |first=Jeff H. |title=Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles |date=2009 |publisher=ACM |isbn=978-1-60558-752-3 |pages=87–102 |chapter=Automatically patching errors in deployed software |citeseerx=10.1.1.157.5877 |doi=10.1145/1629575.1629585 |display-authors=etal |s2cid=7597529}}</ref> which does repair on x86 code, with x86 binary patches.
== Search space ==
In essence, automatic bug fixing is a search activity, whether deductive-based or heuristic-based. The search space of automatic bug fixing is composed of all edits that can be possibly made to a program. There have been studies to understand the structure of this search space. Qi et al.<ref>{{Cite book |last1=Qi |first1=Yuhua |title=The strength of random search on automated program repair |last2=Mao |first2=Xiaoguang |last3=Lei |first3=Yan |last4=Dai |first4=Ziying |last5=Wang |first5=Chengsong |date=2014-05-31 |publisher=ACM |isbn=9781450327565 |pages=254–265 |doi=10.1145/2568225.2568254 |s2cid=14976851}}</ref> showed that the original fitness function of Genprog is not better than random search to drive the search. Long et al.'s<ref name="spaceanalysis" /> study indicated that correct patches can be considered as sparse in the search space and that incorrect overfitting patches are vastly more abundant (see also discussion about overfitting below).
== Overfitting ==
Sometimes, in test-suite based program repair, tools generate patches that pass the test suite, yet are actually incorrect, this is known as the "overfitting" problem.<ref name="overfitting">{{Cite book |last1=Smith |first1=Edward K. |title=Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering |last2=Barr |first2=Earl T. |last3=Le Goues |first3=Claire|author3-link=Claire Le Goues |last4=Brun |first4=Yuriy |date=2015 |publisher=ACM |isbn=978-1-4503-3675-8 |series=ESEC/FSE 2015 |___location=New York, New York |pages=532–543 |chapter=Is the Cure Worse Than the Disease? Overfitting in Automated Program Repair |doi=10.1145/2786805.2786825 |s2cid=6300790}}</ref> "Overfitting" in this context refers to the fact that the patch overfits to the test inputs. There are different kinds of overfitting: incomplete fixing means that only some buggy inputs are fixed, regression introduction means some previously working features are broken after the patch (because they were poorly tested). Early prototypes for automatic repair suffered a lot from overfitting: on the Manybugs C benchmark, Qi et al.<ref name="kali" /> reported that 104/110 of plausible GenProg patches were overfitting. In the context of synthesis-based repair, Le et al.<ref>{{Cite journal |last1=Le |first1=Xuan Bach D. |last2=Thung |first2=Ferdian |last3=Lo |first3=David |last4=Goues |first4=Claire Le |date=2018-03-02 |title=Overfitting in semantics-based automated program repair |url=https://ink.library.smu.edu.sg/sis_research/3986 |journal=Empirical Software Engineering |language=en |volume=23 |issue=5 |pages=3007–3033 |doi=10.1007/s10664-017-9577-2 |issn=1382-3256 |s2cid=3635768}}</ref> obtained more than 80% of overfitting patches.
One way to avoid overfitting is to filter out the generated patches. This can be done based on dynamic analysis.<ref>{{Cite book|last1=Xin|first1=Qi|last2=Reiss|first2=Steven P.|title=Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis |chapter=Identifying test-suite-overfitted patches through test case generation |date=2017-07-10|chapter-url=http://dx.doi.org/10.1145/3092703.3092718|pages=226–236|___location=New York, NY, USA|publisher=ACM|doi=10.1145/3092703.3092718|isbn=978-1-4503-5076-1|s2cid=20562134}}</ref>
Alternatively, Tian et al. propose heuristic approaches to assess patch correctness.<ref>{{cite news |last1=Tian |first1=Haoye |last2=Liu |first2=Kui |last3=Kaboré |first3=Abdoul Kader |last4=Koyuncu |first4=Anil |last5=Li |first5=Li |last6=Klein |first6=Jacques |last7=Bissyandé |first7=Tegawendé F. |title=Evaluating representation learning of code changes for predicting patch correctness in program repair |url=https://dl.acm.org/doi/abs/10.1145/3324884.3416532 |work=Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering |publisher=Association for Computing Machinery |date=27 January 2021 |pages=981–992 |doi=10.1145/3324884.3416532|isbn=9781450367684 }}</ref><ref>{{cite book |last1=Tian |first1=Haoye |last2=Tang |first2=Xunzhu |last3=Habib |first3=Andrew |last4=Wang |first4=Shangwen |last5=Liu |first5=Kui |last6=Xia |first6=Xin |last7=Klein |first7=Jacques |last8=BissyandÉ |first8=TegawendÉ F. |title=Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering |chapter=Is this Change the Answer to that Problem?: Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness |date=5 January 2023 |pages=1–13 |doi=10.1145/3551349.3556914 |chapter-url=https://dl.acm.org/doi/abs/10.1145/3551349.3556914 |publisher=Association for Computing Machinery|s2cid=251403079 |arxiv=2208.04125 |isbn=9781450394758 }}</ref>
== Limitations of automatic bug-fixing ==
Automatic bug-fixing techniques that rely on a test suite do not provide patch correctness guarantees, because the test suite is incomplete and does not cover all cases.<ref name="kali" /> A weak test suite may cause generate-and-validate techniques to produce validated but incorrect patches that have negative effects such as eliminating desirable functionalities, causing memory leaks, and introducing security vulnerabilities.<ref name="kali" /> One possible approach is to amplify the failing test suite by automatically generating further test cases that are then labelled as passing or failing. To minimize the human labelling effort, an automatic [[test oracle]] can be trained that gradually learns to automatically classify test cases as passing or failing and only engages the bug-reporting user for uncertain cases.<ref name="learn2fix">{{Cite book |last1=Böhme |first1=Marcel |title=Proceedings of the 13th International Conference on Software Testing, Validation and Verification |last2=Geethal |first2=Charaka |last3=Pham |first3=Van-Thuan |date=2020 |publisher=IEEE |isbn=978-1-7281-5778-8 |series=ICST 2020 |___location=Porto, Portugal |pages=274–285 |chapter=Human-In-The-Loop Automatic Program Repair |arxiv=1912.07758 |doi=10.1109/ICST46399.2020.00036 |s2cid=209386817}}</ref>
A limitation of generate-and-validate repair systems is the search space explosion.<ref name="spaceanalysis">{{Cite book |last1=Long |first1=Fan |title=Proceedings of the 38th International Conference on Software Engineering |last2=Rinard |first2=Martin |date=2016 |publisher=ACM |isbn=978-1-4503-3900-1 |series=ICSE '16 |___location=New York, New York |pages=702–713 |chapter=An Analysis of the Search Spaces for Generate and Validate Patch Generation Systems |arxiv=1602.05643 |doi=10.1145/2884781.2884872 |hdl=1721.1/113656 |s2cid=7426809}}</ref> For a program, there are a large number of statements to change and for each statement there are a large number of possible modifications. State-of-the-art systems address this problem by assuming that a small modification is enough for fixing a bug, resulting in a search space reduction.
The limitation of approaches based on symbolic analysis<ref name="semfix" /><ref name="angelix" /> is that real world programs are often converted to intractably large formulas especially for modifying statements with [[side effect (computer science)|side effects]].
==
Benchmarks of bugs typically focus on one specific programming language.
In C, the Manybugs benchmark collected by GenProg authors contains 69 real world defects and it is widely used to evaluate many other bug-fixing tools for C.<ref name=genprog2012 /><ref name=prophet /><ref name=spr /><ref name=angelix />
In [[Java (programming language)|Java]], the main benchmark is Defects4J now extensively used in most research papers on program repair for Java.<ref name="capgen">{{Cite book |last1=Wen |first1=Ming |last2=Chen |first2=Junjie |last3=Wu |first3=Rongxin |last4=Hao |first4=Dan |last5=Cheung |first5=Shing-Chi |title=Proceedings of the 40th International Conference on Software Engineering |chapter=Context-aware patch generation for better automated program repair |date=2018 |___location=New York, New York, USA |publisher=ACM Press |pages=1–11 |doi=10.1145/3180155.3180233 |isbn=9781450356381 |s2cid=3374770|url=https://repository.hkust.edu.hk/ir/Record/1783.1-92186 |chapter-url=https://repository.ust.hk/ir/Record/1783.1-92186 }}</ref><ref>{{Cite book |last1=Hua |first1=Jinru |last2=Zhang |first2=Mengshi |last3=Wang |first3=Kaiyuan |last4=Khurshid |first4=Sarfraz |title=Proceedings of the 40th International Conference on Software Engineering |chapter=Towards practical program repair with on-demand candidate generation |date=2018 |___location=New York, New York, USA |publisher=ACM Press |pages=12–23 |doi=10.1145/3180155.3180245 |isbn=9781450356381 |s2cid=49666327|doi-access=free }}</ref> Alternative benchmarks exist, such as the Quixbugs benchmark,<ref>{{Cite book |last1=Lin |first1=Derrick |last2=Koppel |first2=James |last3=Chen |first3=Angela |last4=Solar-Lezama |first4=Armando |title=Proceedings Companion of the 2017 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity |chapter=QuixBugs: A multi-lingual program repair benchmark set based on the quixey challenge |date=2017 |___location=New York, New York, USA |publisher=ACM Press |pages=55–56 |doi=10.1145/3135932.3135941 |isbn=9781450355148 |doi-access=free}}</ref> which contains original bugs for program repair. Other benchmarks of Java bugs include Bugs.jar,<ref>{{Cite book |last1=Saha |first1=Ripon K. |last2=Lyu |first2=Yingjun |last3=Lam |first3=Wing |last4=Yoshida |first4=Hiroaki |last5=Prasad |first5=Mukul R. |title=Proceedings of the 15th International Conference on Mining Software Repositories |chapter=Bugs.jar |date=2018 |chapter-url=http://dl.acm.org/citation.cfm?doid=3196398.3196473 |series=MSR '18 |language=en |pages=10–13 |doi=10.1145/3196398.3196473 |isbn=9781450357166 |s2cid=50770093}}</ref> based on past commits.
== Example tools ==
Automatic bug-fixing is an active research topic in computer science. There are many implementations of various bug-fixing techniques especially for C and Java programs. Note that most of these implementations are research prototypes for demonstrating their techniques, i.e., it is unclear whether their current implementations are ready for industrial usage or not.
=== C ===
* ClearView:<ref name=clearview /> A generate-and-validate tool of generating binary patches for deployed systems. It is evaluated on 10 security vulnerability cases. A later study shows that it generates correct patches for at least 4 of the 10 cases.<ref name=kali />
* GenProg:<ref name=genprog2009 /><ref name=genprog2012 /> A seminal generate-and-validate bug-fixing tool. It has been extensively studied in the context of the ManyBugs benchmark.
* SemFix:<ref name=semfix /> The first solver-based bug-fixing tool for C.
* CodePhage:<ref name=codephage /> The first bug-fixing tool that directly transfer code across programs to generate patch for C program. Note that although it generates C patches, it can extract code from [[machine code|binary programs]] without source code.<ref name=codephage />
* LeakFix:<ref name=leakfix /> A tool that automatically fixes memory leaks in C programs.
* Prophet:<ref name=prophet /> The first generate-and-validate tool that uses machine learning techniques to learn useful knowledge from past human patches to recognize correct patches. It is evaluated on the same benchmark as GenProg and generate correct patches (i.e., equivalent to human patches) for 18 out of 69 cases.<ref name=prophet />
* SearchRepair:<ref name=searchrepair /> A tool for replacing buggy code using snippets of code from elsewhere. It is evaluated on the IntroClass benchmark<ref name="introclassmanybugs">{{Cite journal |last1=Le Goues |first1=Claire|author1-link=Claire Le Goues |last2=Holtschulte |first2=Neal |last3=Smith |first3=Edward |last4=Brun |first4=Yuriy |last5=Devanbu |first5=Premkumar |last6=Forrest |first6=Stephanie |last7=Weimer |first7=Westley |date=2015 |title=The Many ''Bugs'' and Intro ''Class'' Benchmarks for Automated Repair of C Programs |journal=IEEE Transactions on Software Engineering |volume=41 |issue=12 |pages=1236–1256 |doi=10.1109/TSE.2015.2454513 |doi-access=free}}</ref> and generates much higher quality patches on that benchmark than GenProg, RSRepair, and AE.
* Angelix:<ref name=angelix /> An improved solver-based bug-fixing tool. It is evaluated on the GenProg benchmark. For 10 out of the 69 cases, it generate patches that is equivalent to human patches.
* Learn2Fix:<ref name=learn2fix /> The first human-in-the-loop semi-automatic repair tool. Extends GenProg to learn the condition under which a semantic bug is observed by systematic queries to the user who is reporting the bug. Only works for programs that take and produce integers.
=== Java ===
* PAR:<ref name=par /> A generate-and-validate tool that uses a set of manually defined fix templates.
* QACrashFix:<ref name="QAFix">{{Cite book |last1=Gao |first1=Qing |title=2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) |last2=Zhang |first2=Hansheng |last3=Wang |first3=Jie |last4=Xiong |first4=Yingfei |last5=Zhang |first5=Lu |last6=Mei |first6=Hong |date=2015 |publisher=IEEE |isbn=978-1-5090-0025-8 |pages=307–318 |chapter=Fixing Recurring Crash Bugs via Analyzing Q&A Sites |doi=10.1109/ASE.2015.81 |s2cid=2513924}}</ref> A tool that fixes Java crash bugs by mining fixes from Q&A web site.
* ARJA:<ref name="arja">{{Cite journal |last1=Yuan |first1=Yuan |last2=Banzhaf |first2=Wolfgang |date=2020 |title=ARJA: Automated Repair of Java Programs via Multi-Objective Genetic Programming |url=https://doi.org/10.1109/TSE.2018.2874648 |journal=IEEE Transactions on Software Engineering |volume=46 |issue=10 |pages=1040–1067 |arxiv=1712.07804 |doi=10.1109/TSE.2018.2874648|s2cid=25222219 }}</ref> A repair tool for Java based on multi-objective genetic programming.
* NpeFix:<ref name="npefix">{{Cite book |last=Durieux |first=Thomas |title=2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER) |date=2017 |isbn=978-1-5090-5501-2 |pages=349–358 |chapter=Dynamic Patch Generation for Null Pointer Exceptions Using Metaprogramming |arxiv=1812.00409 |doi=10.1109/SANER.2017.7884635 |s2cid=2736203}}</ref> An automatic repair tool for NullPointerException in Java, available [https://github.com/Spirals-Team/npefix on Github].
===
* AutoFixE:<ref name=autofixe /> A bug-fixing tool for [[Eiffel programming language|Eiffel language]]. It relies the contracts (i.e., a form of formal specification) in Eiffel programs to validate generated patches.
*Getafix:<ref name=":0" /> Operates purely on [[Abstract syntax tree|AST]] transformations and thus requires only a [[parser]] and formatter. At Facebook it has been applied to [[Hack (programming language)|Hack]], [[Java (programming language)|Java]] and [[Objective-C]].
=== Proprietary ===
* DeepCode integrates public and private [[GitHub]], [[GitLab]] and [[Bitbucket]] [[Software repository|repositories]] to identify code-fixes and improve software.<ref>{{Cite web |title=AI is coming for your coding job |url=https://sifted.eu/articles/ai-is-coming-for-your-coding-job/ |access-date=2019-04-15 |website=Sifted |date=13 March 2019 |language=en-US}}</ref>
* [[Kodezi, Inc.|Kodezi]] utilizes opensource data from [[GitHub]] [[Software repository|repositories]], [[Stack Overflow]], and private trained models to analyze code, provide solutions, and descriptions about the coding bugs instantly.<ref>{{Cite web |title=Ishraq Khan, Revolutionizing the Programming Scene in 2021 |url=https://www.techtimes.com/articles/265325/20210913/ishraq-khan-revolutionizing-the-programming-scene-in-2021.htm |access-date=2022-10-15 |website=TechTimes|date=13 September 2019 |language=en-US}}</ref>
== References ==
{{Reflist}}
== External links ==
* {{URL|https://program-repair.org/}} datasets, tools, etc., related to automated program repair research.
[[Category:Debugging]]
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