fine-GRAPE: fine-Grained APi usage Extractor An Approach and Dataset to Investigate API Usage

84 Pages·2015·0.67 MB·English

fine-GRAPE: fine-Grained APi usage
Extractor An Approach and Dataset to
Investigate API Usage
Version of November 16, 2015
Anand Ashok Sawant

fine-GRAPE: fine-Grained APi usage
Extractor An Approach and Dataset to
Investigate API Usage
THESIS
submittedinpartialfulfillmentofthe
requirementsforthedegreeof
MASTEROFSCIENCE
in
COMPUTERSCIENCE
by
AnandAshokSawant
borninMumbai,India
SoftwareEngineeringResearchGroup
DepartmentofSoftwareTechnology
FacultyEEMCS,DelftUniversityofTechnology
Delft,theNetherlands
www.ewi.tudelft.nl
(cid:13)c 2015 AnandAshokSawant. Allrightsreserved.
fine-GRAPE: fine-Grained APi usage
Extractor An Approach and Dataset to
Investigate API Usage
Author: AnandAshokSawant
Studentid: 4048830
Email: [email protected]
Abstract
AnApplicationProgrammingInterface(API)providesaspecificsetoffunctional-
itiestoadeveloper,withtheaimofenablingreuse. APIshavebeeninvestigatedfrom
differentanglessuchaspopularityusageandevolution, togetabetterunderstanding
of their various characteristics. For such studies software repositories are mined for
APIusageexamples. However, theminingalgorithmsusedforsuchpurposesdonot
taketypeinformationintoaccount,thusmakingtheresultsimprecise.
Inthisthesis,weaimtorectifythisbyintroducingfine-GRAPE,anapproachthat
producesfine-grainedAPIusageinformationbytakingadvantageoftypeinformation
while mining API method invocations and annotations. fine-GRAPE establishes a
connectionbetweenamethodinvocationandtheclassoftheAPItowhichthemethod
belongs. By means of fine-GRAPE, we investigate API usages from Java projects
hostedonGitHub.WeselectfiveofthemostpopularAPIsacrossGitHubJavaprojects
andcollecthistoricalAPIusageinformationbyminingboththereleasehistoryofthese
APIsandthecodehistoryofeveryprojectthatusesthem.
Weusetheresultingdatasettoperformfourseparateanalyses. Thefirstmeasures
the lag time of each client by leveraging the version information that has been col-
lected. We see that in most cases clients do not upgrade the version of the API that
they are using to the latest version. The consequence of this is that the lag time that
eachclientdisplaysisquitehigh. ThesecondstudyinvestigatesthepercentageofAPI
featuresthatareusedbyusingthetypeinformationinthedataset. Theresultsofthis
study show that a very small percentage of an API is actually used by clients in the
real world. Our third study aims to show the relation between popular features and
softwarequality. Finally, thefourthstudyanalyzesthereactionofclientstothedep-
recationofAPIartifacts. Ourdeprecationstudyshowsthatmostclientsdonotreally
reacttodeprecatedentities.
ThesisCommittee:
Chair: Prof. Dr. A.vanDeursen,FacultyEEMCS,TUDelft
Universitysupervisor: Dr. A.Bacchelli,FacultyEEMCS,TUDelft
CommitteeMember: Dr. A.Orso,ARKTOS,GeorgiaTech
CommitteeMember: Dr. R.Robbes,FacultyPLEAID,UCChile
CommitteeMember: Dr. A.Bozzon,FacultyEEMCS,TUDelft
ii
Preface
This thesis represents my work from the period of September 2014 to November 2015
duringthesecondyearofmyMastersprogramattheDelftUniversityofTechnology. Inmy
thesis I present a technique that I developed which aids in the collection of type-checked
API usages from open source code hosting platforms called fine-GRAPE. Based on this
approachIconstructalargedatasetthatcontainsusagedataforfivemainstreamJavaAPIs.
Iperformfourstudiesbasedonthisdataset,andeachofthestudiesconductedarepresented
inachapterinthisthesisandisbasedonaresearchpaperthatiseitherpublishedorunder
submission. Most of the work for this thesis was done in the Netherlands at the Delft
University of Technology under the supervision of Dr. Alberto Bacchelli. Some work
was done in Atlanta at the Georgia Institute of Technology under the supervision of Dr.
Alessandro Orso. Also, a special thanks to Dr. Romain Robbes for his help and guidance
fortheworkonthenatureofdeprecationthatisoneofthestudiesdoneonthedataset.
Thecreationofthisthesiswasawonderfulandrewardingexperience. Duringitscourse
IgottheopportunitytomovetheUnitedStatesforaperiodoffivemonths. Ialsowaslucky
enough to get to go my first ever Software Engineering conference during my masters. I
would like to thank my family and my friends for being there for me whenever I needed
helpandforputtingupwithmyshenanigans.
AnandAshokSawant
Delft,theNetherlands
November16,2015
iii

Contents
Preface iii
Contents v
ListofFigures vii
1 Introduction 1
2 BackgroundandRelatedWork 3
2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 ExistingAPIminingtechniques . . . . . . . . . . . . . . . . . . . . . . . 4
2.3 UsecasesforAPIUsagedatasets . . . . . . . . . . . . . . . . . . . . . . . 5
3 TheApproach-fine-GRAPE 7
3.1 Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.3 Miningofcoarsegrainedusage . . . . . . . . . . . . . . . . . . . . . . . . 7
3.4 Fine-grainedAPIusage . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.5 ADatasetforAPIUsage . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.6 Comparisontoexistingdatasets . . . . . . . . . . . . . . . . . . . . . . . 14
4 InvestigatingPropertiesofAPIs 17
4.1 Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.3 Study1: DoclientsofAPIsmigratetoanewversionoftheAPI? . . . . . . 18
4.4 Study2: HowmuchofanAPIisbroadlyused? . . . . . . . . . . . . . . . 24
4.5 Study3: ArepopularpartsofanAPImorelikelytohavebugs? . . . . . . . 28
4.6 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
5 APIDeprecation 37
v
CONTENTS
5.1 Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.3 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
5.4 ResearchGoals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5.5 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
6 ConclusionsandFutureWork 61
6.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
6.2 Futurework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
6.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Bibliography 65
vi