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% Software engineering bibliography
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Maintenance
%%%
@Article{Putnam78,
author = "Lawrence H. Putnam",
title = "A general empirical solution to the macro software sizing
and estimating problem",
journal = TSE,
year = 1978,
volume = 4,
number = 4,
pages = "345--361",
month = jul
}
@Article{BalzerCG83,
author = "R. Balzer and T. E. Cheatham, Jr. and C. Green",
title = "Software technology in the 1990's: Using a new paradigm",
journal = "Computer",
year = 1983,
volume = 16,
number = 11,
pages = "39--45",
month = nov
}
@Article{Boehm87:top10,
author = "Barry W. Boehm",
title = "Industrial software metrics top 10 list",
journal = IEEESoftware,
year = 1987,
volume = 4,
number = 5,
pages = "84--85",
month = sep
}
@Article{Erlikh2000,
author = "Erlikh, Len",
title = "Leveraging legacy system dollars for e-business",
journal = "IT Professional",
year = 2000,
volume = 2,
number = 3,
pages = "17--23",
month = may,
}
@Article{Eastwood1993,
author = "A. Eastwood",
title = "Firm fires shots at legacy systems",
journal = "Computing Canada",
year = 1993,
volume = 19,
number = 2,
pages = 17,
}
@Article{Moad1990,
author = "J. Moad",
title = "Maintaining the competitive edge",
journal = "DATAMATION",
year = 1990,
pages = "61--66",
month = feb,
}
@Article{LientzS1981,
author = "Lientz, Bennet P. and Swanson, E. Burton",
title = "Problems in application software maintenance",
journal = CACM,
year = 1981,
volume = 24,
number = 11,
pages = "763--769",
month = nov,
}
@Book{ZelkowitzSG1979,
author = "Zelkowitz, Marvin V. and Shaw, Alan C. and Gannon, John D.",
title = "Principles of Software Engineering and Design",
publisher = "Prentice Hall Professional Technical Reference",
year = 1979,
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Refactoring
%%%
@TechReport{Griswold91,
author = "William G. Griswold",
title = "Program Restructuring To Aid Software Maintenance",
institution = UWCSE,
year = 1991,
number = "91-08-04",
address = UWCSEaddr,
month = aug,
note = "PhD dissertation"
}
@TechReport{Opdyke92,
author = "W. F. Opdyke",
title = "Refactoring: A Program Restructuring Aid in Designing
Object-Oriented Applications Frameworks",
institution = "University of Illinois at Urbana-Champaign, Dept. of Computer Science",
year = 1992,
number = 1759,
note = "PhD dissertation"
}
@book{Fowler2000,
title = {Refactoring: Improving the Design of Existing Code},
author = {Martin Fowler},
publisher = {Addison-Wesley},
year = {2000}
}
@InProceedings{Parr94,
author = "Terence J. Parr",
title = "An overview of {SORCERER}: A simple tree-parser generator",
crossref = "CC94",
NEEDpages = "",
}
@Article{MensT2004,
author = "Tom Mens and Tom Tourw{\'e}",
authorASCII = "Tom Mens and Tom Tourwe",
title = "A survey of software refactoring",
journal = TSE,
year = 2004,
volume = 30,
number = 2,
pages = "126--139",
month = feb
}
@InProceedings{StreckenbachS2004,
author = "Mirko Streckenbach and Gregor Snelting",
title = "Refactoring class hierarchies with {KABA}",
crossref = "OOPSLA2004",
pages = "315--330",
}
@InProceedings{HenkelD2005,
author = "Johannes Henkel and Amer Diwan",
title = "CatchUp! Capturing and replaying refactorings to support
{API} evolution",
crossref = "ICSE2005",
pages = "274--283",
abstract =
"Library developers who have to evolve a library to accommodate changing
requirements often face a dilemma: Either they implement a clean, efficient
solution but risk breaking client code, or they maintain compatibility with
client code, but pay with increased design complexity and thus higher
maintenance costs over time.
\par
We address this dilemma by presenting a lightweight approach for evolving
application programming interfaces (APIs), which does not depend on version
control or configuration management systems. Instead, we capture API
actions as a developer evolves an API. Users of the API can then replay the
refactorings to bring their client software components up to date.
\par
We present CatchUp!, an implementation of our approach that captures and
replays refactoring actions within an integrated development environment
semi-automatically. Our experiments suggest that our approach could be
valuable in practice.",
}
@InProceedings{MurphyHillPB2009,
author = "Murphy-Hill, Emerson and Parnin, Chris and Black, Andrew P.",
title = "How we refactor, and how we know it",
crossref = "ICSE2009",
pages = "287--297",
abstract =
"Much of what we know about how programmers refactor in the wild is
based on studies that examine just a few software projects. Researchers
have rarely taken the time to replicate these studies in other contexts
or to examine the assumptions on which they are based. To help put
refactoring research on a sound scientific basis, we draw conclusions
using four data sets spanning more than 13 000 developers, 240 000
tool-assisted refactorings, 2500 developer hours, and 3400 version
control commits. Using these data, we cast doubt on several previously
stated assumptions about how programmers refactor, while validating
others. For example, we find that programmers frequently do not indicate
refactoring activity in commit logs, which contradicts assumptions made
by several previous researchers. In contrast, we were able to confirm
the assumption that programmers do frequently intersperse refactoring
with other program changes. By confirming assumptions and replicating
studies made by other researchers, we can have greater confidence that
those researchers' conclusions are generalizable.",
}
@InProceedings{KashiwaSLBLKU2021,
author = "Kashiwa, Yutaro and Shimizu, Kazuki and Lin, Bin and Bavota, Gabriele and Lanza, Michele and Kamei, Yasutaka and Ubayashi, Naoyasu",
title = "Does Refactoring Break Tests and to What Extent?",
crossref = "ICSME2021",
pages = "171-182",
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Programmer productivity: lines of code per day
%%%
@Book{McConnell2006,
author = "Steve McConnell",
title = "Software Estimation: Demystifying the Black Art",
publisher = "Microsoft Press",
year = 2006,
}
@Book{Jones2011,
author = "Capers Jones",
title = "The Economics of Software Quality",
publisher = "Addison-Wesley",
year = 2011,
}
@Misc{Su2006,
author = "Philip Su",
title = "Broken {Windows} Theory",
howpublished = "\url{http://blogs.msdn.com/b/philipsu/archive/2006/06/14/631438.aspx}",
month = jun,
year = 2006,
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Experimental methodology
%%%
@Article{HostRW2000,
author = "H{\"o}st, Martin and Regnell, Bj{\"o}rn and Wohlin, Claes",
authorASCII = "Host, Martin and Regnell, Bjorn and Wohlin, Claes",
title = "Using Students As Subjects---A Comparative Study of Students and Professionals in Lead-Time Impact Assessment",
journal = JEmpiricalSE,
year = 2000,
volume = 5,
number = 3,
pages = "201--214",
month = nov,
abstract =
"In many studies in software engineering students are used instead of
professional software developers, although the objective is to draw
conclusions valid for professional software developers. This paper presents
a study where the difference between the two groups is evaluated. People
from the two groups have individually carried out a non-trivial software
engineering judgement task involving the assessment of how ten different
factors affect the lead-time of software development projects. It is found
that the differences are only minor, and it is concluded that software
engineering students may be used instead of professional software
developers under certain conditions. These conditions are identified and
described based on generally accepted criteria for validity evaluation of
empirical studies.",
}
@InProceedings{Runeson2003,
author = "Runeson, Per",
title = "Using students as experiment subjects -- An analysis on graduate and freshmen student data",
booktitle = "Proceedings of the 7th International Conference on Empirical Assessment in Software Engineering",
year = 2003,
pages = "95-102",
month = apr # "~8--10,",
address = "Keele, UK",
abstract =
"The question whether students can be used as subjects in
software engineering experiments is debated. In order to
investigate the feasibility of using students as subjects, a
study is conducted in the context of the Personal Software
Process (PSP) in which the performance of freshmen students
and graduate students are compared and also related
to another study in an industrial setting. The hypothesis is
that graduate students perform similarly to industry personnel,
while freshmen student's performance differ. A
quantitative analysis compares the freshmen' and graduate
students. The improvement trends are also compared to
industry data, although limited data access does not allow a
full comparison. It can be concluded that very much the
same improvement trends can be identified for the three
groups. However, the dispersion is larger in the freshmen
group. The absolute levels of the measured characteristics
are significantly different between the student groups primarily
with respect to time, i.e. graduate students do the
tasks in shorter time. The data does not give a sufficient
answer to the hypothesis, but is a basis for further studies
on the issue.",
}
@InProceedings{SalmanTMJ2015,
author = "Iflaah Salman and Ay{\c{s}}e {Tosun Misirli} and Natalia Juristo",
authorASCII = "Iflaah Salman and Ayse Tosun Misirli and Natalia Juristo",
title = "Are Students Representatives of Professionals in Software Engineering Experiments?",
crossref = "ICSE2015",
pages = "666-676",
}
@Article{Kitchenham:2009:SLR:1465742.1466091,
author = "Kitchenham, Barbara and O. Pearl Brereton and Budgen, David and Turner, Mark and Bailey, John and Linkman, Stephen",
title = "Systematic literature reviews in software engineering -- A systematic literature review",
journal = "Inf. Softw. Technol.",
year = 2009,
volume = 51,
number = 1,
pages = "7--15",
month = jan,
}
@article{Kitchenham:2010:SLR:1808352.1808650,
author = {Kitchenham, Barbara and Pretorius, Rialette and Budgen, David and Pearl Brereton, O. and Turner, Mark and Niazi, Mahmood and Linkman, Stephen},
title = {Systematic Literature Reviews in Software Engineering - A Tertiary Study},
journal = {Inf. Softw. Technol.},
issue_date = {August, 2010},
volume = {52},
number = {8},
month = aug,
year = {2010},
issn = {0950-5849},
pages = {792--805},
numpages = {14},
url = {http://dx.doi.org/10.1016/j.infsof.2010.03.006},
doi = {10.1016/j.infsof.2010.03.006},
acmid = {1808650},
publisher = {Butterworth-Heinemann},
address = {Newton, MA, USA},
keywords = {Mapping study, Software engineering, Systematic literature review, Tertiary study},
}
@inproceedings{Devanbu:2016:BEE:2884781.2884812,
author = {Devanbu, Prem and Zimmermann, Thomas and Bird, Christian},
title = {Belief \& Evidence in Empirical Software Engineering},
booktitle = {Proceedings of the 38th International Conference on Software Engineering},
series = {ICSE '16},
year = {2016},
isbn = {978-1-4503-3900-1},
address = {Austin, Texas},
pages = {108--119},
numpages = {12},
url = {https://doi.acm.org/10.1145/2884781.2884812},
doi = {10.1145/2884781.2884812},
acmid = {2884812},
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Defect prediction (fault prediction)
%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Fault localization
%%%
@InProceedings{JonesH2005,
author = "James A. Jones and Mary Jean Harrold",
title = "Empirical evaluation of the {Tarantula} automatic fault-localization technique",
crossref = "ASE2005",
pages = "273--282",
}
@InProceedings{ParninO2011,
author = "Parnin, Chris and Orso, Alessandro",
title = "Are automated debugging techniques actually helping programmers?",
crossref = "ISSTA2011",
pages = "199--209",
}
@InProceedings{RenierisR2003,
author = "Manos Renieris and Steven P. Reiss",
title = "Fault localization with nearest neighbor queries",
crossref = "ASE2003",
pages = "30--39",
}
@InProceedings{SteimannFA2013,
author = "Steimann, Friedrich and Frenkel, Marcus and Abreu, Rui",
title = "Threats to the validity and value of empirical assessments of the accuracy of coverage-based fault locators",
crossref = "ISSTA2013",
pages = "314--324",
}
@InProceedings{ZhangZK2013,
author = "Zhang, Lingming and Zhang, Lu and Khurshid, Sarfraz",
title = "Injecting mechanical faults to localize developer faults for evolving software",
crossref = "OOPSLA2013",
pages = "765--784",
}
@InProceedings{JonesHS2002,
author = "Jones, James A. and Harrold, Mary Jean and Stasko, John",
title = "Visualization of test information to assist fault localization",
crossref = "ICSE2002",
pages = "467--477",
}
@Unpublished{YooNH2011,
author = "Yoo, Shin and Nilsson, Robert and Harman, Mark",
title = "Faster fault finding at {Google} using multi objective regression test optimisation",
note = "Unpublished paper accompanying industrial track talk at ESEC/FSE 2011",
month = sep,
year = 2011,
}
@Article{DiGiuseppeJ2015,
author = "DiGiuseppe, Nicholas and Jones, James A.",
title = "Fault Density, Fault Types, and Spectra-based Fault Localization",
journal = JEmpiricalSE,
year = 2015,
volume = 20,
number = 4,
pages = "928--967",
month = aug,
}
@InProceedings{KochharXLL2016,
author = "Kochhar, Pavneet Singh and Xia, Xin and Lo, David and Li, Shanping",
title = "Practitioners' expectations on automated fault localization",
crossref = "ISSTA2016",
pages = "165--176",
}
@InProceedings{LongR2016:ICSE,
author = "Long, Fan and Rinard, Martin",
title = "An analysis of the search spaces for generate and validate patch generation systems",
crossref = "ICSE2016",
pages = "702--713",
}
@InProceedings{WongWQZ2008,
author = "Wong, Eric and Wei, Tingting and Qi, Yu and Zhao, Lei",
title = "A crosstab-based statistical method for effective fault localization",
crossref = "ICST2008",
pages = "42-51",
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Automated program repair (APR)
%%%
@InProceedings{LeGouesDVFW2012,
author = "Le Goues, Claire and Dewey-Vogt, Michael and Forrest, Stephanie and Weimer, Westley",
title = "A systematic study of automated program repair: fixing 55 out of 105 bugs for \$8 each",
crossref = "ICSE2012",
pages = "3--13",
abstract =
"There are more bugs in real-world programs than human programmers can
realistically address. This paper evaluates two research questions: ``What
fraction of bugs can be repaired automatically?'' and ``How much does it
cost to repair a bug automatically?'' In previous work, we presented
GenProg, which uses genetic programming to repair defects in off-the-shelf
C programs. To answer these questions, we: (1) propose novel algorithmic
improvements to GenProg that allow it to scale to large programs and find
repairs 68\% more often, (2) exploit GenProg's inherent parallelism using
cloud computing resources to provide grounded, human-competitive cost
measurements, and (3) generate a large, indicative benchmark set to use for
systematic evaluations. We evaluate GenProg on 105 defects from 8
open-source programs totaling 5.1 million lines of code and involving
10,193 test cases. GenProg automatically repairs 55 of those 105
defects. To our knowledge, this evaluation is the largest available of its
kind, and is often two orders of magnitude larger than previous work in
terms of code or test suite size or defect count. Public cloud computing
prices allow our 105 runs to be reproduced for \$403; a successful repair
completes in 96 minutes and costs \$7.32, on average.",
}
@InProceedings{QiLAR2015,
author = "Qi, Zichao and Long, Fan and Achour, Sara and Rinard, Martin",
title = "An analysis of patch plausibility and correctness for generate-and-validate patch generation systems",
crossref = "ISSTA2015",
pages = "24--36",
doi = {10.1145/2771783.2771791},
}
@InProceedings{TianR2017,
author = "Tian, Yuchi and Ray, Baishakhi",
title = "Automatically diagnosing and repairing error handling bugs in {C}",
crossref = "FSE2017",
year = 2017,
pages = "752--762",
}
@Article{Motwani2017,
author="Motwani, Manish
and Sankaranarayanan, Sandhya
and Just, Ren{\'e}
and Brun, Yuriy",
title="Do automated program repair techniques repair hard and important bugs?",
journal=JEmpiricalSE,
year="2017",
month="Nov",
day="18",
abstract="Existing evaluations of automated repair techniques focus on the fraction of the defects for which the technique can produce a patch, the time needed to produce patches, and how well patches generalize to the intended specification. However, these evaluations have not focused on the applicability of repair techniques and the characteristics of the defects that these techniques can repair. Questions such as ``Can automated repair techniques repair defects that are hard for developers to repair?'' and ``Are automated repair techniques less likely to repair defects that involve loops?'' have not, as of yet, been answered. To address such questions, we annotate two large benchmarks totaling 409 C and Java defects in real-world software, ranging from 22K to 2.8M lines of code, with measures of the defect's importance, the developer-written patch's complexity, and the quality of the test suite. We then analyze relationships between these measures and the ability to produce patches for the defects of seven automated repair techniques ---AE, GenProg, Kali, Nopol, Prophet, SPR, and TrpAutoRepair. We find that automated repair techniques are less likely to produce patches for defects that required developers to write a lot of code or edit many files, or that have many tests relevant to the defect. Java techniques are more likely to produce patches for high-priority defects. Neither the time it took developers to fix a defect nor the test suite's coverage correlate with the automated repair techniques' ability to produce patches. Finally, automated repair techniques are less capable of fixing defects that require developers to add loops and new function calls, or to change method signatures. These findings identify strengths and shortcomings of the state-of-the-art of automated program repair along new dimensions. The presented methodology can drive research toward improving the applicability of automated repair techniques to hard and important bugs.",
issn="1573-7616",
doi="10.1007/s10664-017-9550-0",
url="https://doi.org/10.1007/s10664-017-9550-0"
}
@inproceedings{vanTonder:2018:SAP:3180155.3180250,
author = {van Tonder, Rijnard and Goues, Claire Le},
title = {Static Automated Program Repair for Heap Properties},
booktitle = {Proceedings of the 40th International Conference on Software Engineering},
series = {ICSE '18},
year = {2018},
isbn = {978-1-4503-5638-1},
location = {Gothenburg, Sweden},
pages = {151--162},
numpages = {12},
url = {https://doi.acm.org/10.1145/3180155.3180250},
doi = {10.1145/3180155.3180250},
acmid = {3180250},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {automated program repair, separation logic},
}
@Article{AletiM2021,
author = "Aleti, Aldeida and Martinez, Matias",
title = "{E-APR}: Mapping the effectiveness of automated program repair techniques",
journal = JEmpiricalSE,
year = 2021,
volume = 26,
number = 99,
NEEDpages = "*",
month = jul,
abstract =
"Automated Program Repair (APR) is a fast growing area with numerous new
techniques being developed to tackle one of the most challenging software
engineering problems. APR techniques have shown promising results, giving
us hope that one day it will be possible for software to repair itself. In
this paper, we focus on the problem of objective performance evaluation of
APR techniques. We introduce a new approach, Explaining Automated Program
Repair (E-APR), which identifies features of buggy programs that explain
why a particular instance is difficult for an APR technique. E-APR is used
to examine the diversity and quality of the buggy programs used by most
researchers, and analyse the strengths and weaknesses of existing APR
techniques. E-APR visualises an instance space of buggy programs, with each
buggy program represented as a point in the space. The instance space is
constructed to reveal areas of hard and easy buggy programs, and enables
the strengths and weaknesses of APR techniques to be identified.",
}
@INPROCEEDINGS{tan2015relifix,
author={Tan, Shin Hwei and Roychoudhury, Abhik},
booktitle={2015 IEEE/ACM 37th IEEE International Conference on Software Engineering},
title={relifix: Automated Repair of Software Regressions},
year={2015},
volume={1},
number={},
pages={471-482},
doi={10.1109/ICSE.2015.65}}
@Article{KirbasWMKPSVWCBHHW2021,
author = "Kirbas, Serkan and Windels, Etienne and McBello, Olayori and Kells, Kevin and Pagano, Matthew and Szalanski, Rafal and Nowack, Vesna and Winter, Emily Rowan and Counsell, Steve and Bowes, David and Hall, Tracy and Haraldsson, Saemundur and Woodward, John",
title = "On the introduction of automatic program repair in {Bloomberg}",
journal = IEEESoftware,
year = 2021,
volume = 38,
number = 4,
pages = "43-51",
month = jul # "-" # aug,
}
@InProceedings{EldawyLGB2023,
author = "Hadeel Eladawy and Claire {Le Goues} and Yuriy Brun",
title = "Automated program repair, what is it good for? Not absolutely nothing!",
crossref = "ICSE2024",
NEEDpages = "*",
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Clone detection and code similarity
%%%
@InProceedings{BasitRJ2005,
author = "Hamid Abdul Basit and Damith C. Rajapakse and Stan Jarzabek",
title = "Beyond templates: A study of clones in the {STL} and some
general implications",
crossref = "ICSE2005",
pages = "451--459",
abstract =
"Templates (or generics) help us write compact, generic code, which aids
both reuse and maintenance. The STL is a powerful example of how templates
help achieve these goals. Still, our study of the STL revealed substantial,
and in our opinion, counter-productive repetitions (so-called clones)
across groups of similar class or function templates. Clones occurred, as
variations across these similar program structures were irregular and could
not be unified by suitable template parameters in a natural way. We
encountered similar problems in other class libraries as well as in
application programs, written in a range of programming languages. In the
paper, we present quantitative and qualitative results from our study. We
argue that the difficulties we encountered affect programs in general. We
present a solution that can treat such template-unfriendly cases of
redundancies at the meta-level, complementing and extending the power of
language features, such as templates, in areas of generic programming.",
}
@InProceedings{BasitJ2005,
author = "Hamid Abdul Basit and Stan Jarzabek",
title = "Detecting higher-level similarity patterns in programs",
crossref = "FSE2005",
pages = "156--165",
abstract =
"Cloning in software systems is known to create problems during software
maintenance. Several techniques have been proposed to detect the same or
similar code fragments in software, so-called simple clones. While the
knowledge of simple clones is useful, detecting design-level similarities
in software could ease maintenance even further, and also help us identify
reuse opportunities. We observed that recurring patterns of simple clones -
so-called structural clones - often indicate the presence of interesting
design-level similarities. An example would be patterns of collaborating
classes or components. Finding structural clones that signify potentially
useful design information requires efficient techniques to analyze the bulk
of simple clone data and making non-trivial inferences based on the
abstracted information. In this paper, we describe a practical solution to
the problem of detecting some basic, but useful, types of design-level
similarities such as groups of highly similar classes or files. First, we
detect simple clones by applying conventional token-based techniques. Then
we find the patterns of co-occurring clones in different files using the
Frequent Itemset Mining (FIM) technique. Finally, we perform file
clustering to detect those clusters of highly similar files that are likely
to contribute to a design-level similarity pattern. The novelty of our
approach is application of data mining techniques to detect design level
similarities. Experiments confirmed that our method finds many useful
structural clones and scales up to big programs. The paper describes our
method for structural clone detection, a prototype tool called Clone Miner
that implements the method and experimental results."
}
@InProceedings{KimSNM2005,
author = "Miryung Kim and Vibha Sazawal and David Notkin
and Gail C. Murphy",
title = "An empirical study of code clone genealogies",
crossref = "FSE2005",
pages = "187--196",
abstract =
"It has been broadly assumed that code clones are inherently bad and that
eliminating clones by refactoring would solve the problems of code
clones. To investigate the validity of this assumption, we developed a
formal definition of clone evolution and built a clone genealogy tool that
automatically extracts the history of code clones from a source code
repository. Using our tool we extracted clone genealogy information for two
Java open source projects and analyzed their evolution.
\par
Our study contradicts some conventional wisdom about clones. In particular,
refactoring may not always improve software with respect to clones for two
reasons. First, many code clones exist in the system for only a short time;
extensive refactoring of such short-lived clones may not be worthwhile if
they are likely diverge from one another very soon. Second, many clones,
especially long-lived clones that have changed consistently with other
elements in the same group, are not easily refactorable due to programming
language limitations. These insights show that refactoring will not help in
dealing with some types of clones and open up opportunities for
complementary clone maintenance tools that target these other classes of
clones.",
}
@inproceedings{JB10,
Author = {Kevin Jalbert and Jeremy S. Bradbury},
Title = {Using Clone Detection to Identify Bugs in Concurrent Software},
Booktitle = {Proc. of 26th IEEE International Conference on Software Maintenance (ICSM 2010)},
Month = {Sept.},
Year = {2010}
}
@InProceedings{SainiFLYMSBL2019,
author = "Saini, Vaibhav and Farmahinifarahani, Farima and Lu, Yadong and Yang, Di and Martins, Pedro and Sajnani, Hitesh and Baldi, Pierre and Lopes, Cristina V.",
title = "Towards automating precision studies of clone detectors",
crossref = "ICSE2019",
pages = "49-59",
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Debugging
%%%
@inproceedings{10.1145/3586183.3606781,
author = {Alaboudi, Abdulaziz and Latoza, Thomas D.},
title = {Hypothesizer: A Hypothesis-Based Debugger to Find and Test Debugging Hypotheses},
year = {2023},
isbn = {9798400701320},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3586183.3606781},
doi = {10.1145/3586183.3606781},
abstract = {When software defects occur, developers begin the debugging process by formulating hypotheses to explain the cause. These hypotheses guide the investigation process, determining which evidence developers gather to accept or reject the hypothesis, such as parts of the code and program state developers examine. However, existing debugging techniques do not offer support in finding relevant hypotheses, leading to wasted time testing hypotheses and examining code that ultimately does not lead to a fix. To address this issue, we introduce a new type of debugging tool, the hypothesis-based debugger, and an implementation of this tool in Hypothesizer. Hypothesis-based debuggers support developers from the beginning of the debugging process by finding relevant hypotheses until the defect is fixed. To debug using Hypothesizer, developers first demonstrate the defect, generating a recording of the program behavior with code execution, user interface events, network communications, and user interface changes. Based on this information and the developer’s descriptions of the symptoms, Hypothesizer finds relevant hypotheses, analyzes the code to identify relevant evidence to test the hypothesis, and generates an investigation plan through a timeline view. This summarizes all evidence items related to the hypothesis, indicates whether the hypothesis is likely to be true by showing which evidence items were confirmed in the recording, and enables the developer to quickly check evidence in the recording by viewing code snippets for each evidence item. A randomized controlled experiment with 16 professional developers found that, compared to traditional debugging tools and techniques such as breakpoint debuggers and Stack Overflow, Hypothesizer dramatically improved the success rate of fixing defects by a factor of five and decreased the time to debug by a factor of three.},
booktitle = {Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology},
articleno = {73},
numpages = {14},
keywords = {debugging tools, debugging hypotheses, debugging},
location = {<conf-loc>, <city>San Francisco</city>, <state>CA</state>, <country>USA</country>, </conf-loc>},
series = {UIST '23}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Miscellaneous
%%%
@Misc{Borland2004,
author = "Borland",
title = "Making Development A Team Sport",
howpublished = "demo at JavaOne",
month = jun # "~29,",
year = 2004
}
@InProceedings{ChowN96,
author = "Kingsum Chow and David Notkin",
title = "Semi-automatic update of applications in response to
library changes",
crossref = "ICSM96",
pages = "259--368",
}
@InProceedings{MandelinXBK2005,
author = "David Mandelin and Lin Xu and Rastislav Bod{\'\i}k and
Doug Kimmelman",
authorSEARCHABLE = "Rastislav Bodik",
title = "Jungloid mining: Helping to navigate the {API} jungle",
crossref = "PLDI2005",
pages = "48--61",
abstract =
"Reuse of existing code from class libraries and frameworks is often
difficult because APIs are complex and the client code required to use the
APIs can be hard to write. We observed that a common scenario is that the
programmer knows what type of object he needs, but does not know how to
write the code to get the object.
\par
In order to help programmers write API client code more easily, we
developed techniques for synthesizing jungloid code fragments automatically
given a simple query that describes that desired code in terms of input and
output types. A jungloid is simply a unary expression; jungloids are
simple, enabling synthesis, but are also versatile, covering many coding
problems, and composable, combining to form more complex code fragments. We
synthesize jungloids using both API method signatures and jungloids mined
from a corpus of sample client programs.
\par
We implemented a tool, Prospector, based on these techniques. Prospector is
integrated with the Eclipse IDE code assistance feature, and it infers
queries from context so there is no need for the programmer to write
queries. We tested Prospector on a set of real programming problems
involving APIs; Prospector found the desired solution for 18 of 20
problems.We also evaluated Prospector in a user study, finding that
programmers solved programming problems more quickly and with more reuse
when using Prospector than without Prospector."
}
@Article{SanthiarPK2014,
author = "Santhiar, Anirudh and Pandita, Omesh and Kanade, Aditya",
title = "Mining Unit Tests for Discovery and Migration of Math {APIs}",
journal = TOSEM,
year = 2014,
volume = 24,
number = 1,
pages = "4:1--4:33",
month = oct,
}
@InProceedings{GhafariGMT2014,
author = "Mohammad Ghafari and Carlo Ghezzi and Andrea Mocci and Giordano Tamburrelli",
title = "Mining unit tests for code recommendation",
crossref = "ICPC2014",
pages = "142--145",
}
@InProceedings{RutarAF2004,
author = "Nick Rutar and Christian B. Almazan and Jeffrey S. Foster",
title = "A comparison of bug finding tools for {Java}",
crossref = "ISSRE2004",
pages = "245--256",
abstract =
"Bugs in software are costly and difficult to find and fix. In recent years,
many tools and techniques have been developed for automatically finding
bugs by analyzing source code or intermediate code statically (at compile
time). Different tools and techniques have different tradeoffs, but the
practical impact of these tradeoffs is not well understood. In this paper,
we apply five bug finding tools, specifically Bandera, ESC/Java 2,
FindBugs, JLint, and PMD, to a variety of Java programs. By using a variety
of tools, we are able to cross-check their bug reports and warnings. Our
experimental results show that none of the tools strictly subsumes another,
and indeed the tools often find non-overlapping bugs. We discuss the
techniques each of the tools is based on, and we suggest how particular
techniques affect the output of the tools. Finally, we propose a meta-tool
that combines the output of the tools together, looking for particular
lines of code, methods, and classes that many tools warn about.",
}
@Book{GhezziJM91,
author = "Carlo Ghezzi and Mehdi Jazayeri and Dino Mandrioli",
title = "Fundamentals of Software Engineering",
edition = "1",
publisher = "Prentice Hall",
address = "Englewood Cliffs, NJ",
year = "1991",
ISBN = "0-13-818204-3",
}
@Book{Lamb88,
author = "David Alex Lamb",
title = "Software Engineering: Planning for Change",
publisher = "Prentice Hall",
address = "Englewood Cliffs, NJ",
year = 1988
}
@Book{Sommerville:1996a,
author = "Ian Sommerville",
title = "Software Engineering",
publisher = "Addison-Wesley",
year = "1996",
address = "Wokingham, England",
edition = "Fifth",
}
@Book{Pressman92,
author = "Roger S. Pressman",
title = "Software Engineering: A Practitioner's Approach",
publisher = "McGraw-Hill",
year = 1992,
address = "New York",
edition = "Third"
}
@InProceedings{VandevoordeG94,
author = "Mark T. Vandevoorde and John V. Guttag",
title = "Using specialized procedures and specification-based analysis
to reduce the runtime costs of modularity",
booktitle = "Symposium on the Foundations of Software Engineering",
year = 1994,
month = dec
}