FIXME: synchronize with database! An empirical study of data access self-admitted technical debt

Developers sometimes choose design and implementation shortcuts due to the pressure from tight release schedules. However, shortcuts introduce technical debt that increases as the software evolves. The debt needs to be repaid as fast as possible to minimize its impact on software development and sof...

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Published inEmpirical software engineering : an international journal Vol. 27; no. 6
Main Authors Muse, Biruk Asmare, Nagy, Csaba, Cleve, Anthony, Khomh, Foutse, Antoniol, Giuliano
Format Journal Article
LanguageEnglish
Published New York Springer US 01.11.2022
Springer Nature B.V
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Online AccessGet full text
ISSN1382-3256
1573-7616
DOI10.1007/s10664-022-10119-4

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Abstract Developers sometimes choose design and implementation shortcuts due to the pressure from tight release schedules. However, shortcuts introduce technical debt that increases as the software evolves. The debt needs to be repaid as fast as possible to minimize its impact on software development and software quality. Sometimes, technical debt is admitted by developers in comments and commit messages. Such debt is known as self-admitted technical debt (SATD). In data-intensive systems, where data manipulation is a critical functionality, the presence of SATD in the data access logic could seriously harm performance and maintainability. Understanding the composition and distribution of the SATDs across software systems and their evolution could provide insights into managing technical debt efficiently. We present a large-scale empirical study on the prevalence, composition, and evolution of SATD in data-intensive systems. We analyzed 83 open-source systems relying on relational databases as well as 19 systems relying on NoSQL databases. We detected SATD in source code comments obtained from different snapshots of the subject systems. To understand the evolution dynamics of SATDs, we conducted a survival analysis. Next, we performed a manual analysis of 361 sample data-access SATDs, investigating the composition of data-access SATDs and the reasons behind their introduction and removal. We identified 15 new SATD categories, out of which 11 are specific to database access operations. We found that most of the data-access SATDs are introduced in the later stages of change history rather than at the beginning. We also observed that bug fixing and refactoring are the main reasons behind the introduction of data-access SATDs.
AbstractList Developers sometimes choose design and implementation shortcuts due to the pressure from tight release schedules. However, shortcuts introduce technical debt that increases as the software evolves. The debt needs to be repaid as fast as possible to minimize its impact on software development and software quality. Sometimes, technical debt is admitted by developers in comments and commit messages. Such debt is known as self-admitted technical debt (SATD). In data-intensive systems, where data manipulation is a critical functionality, the presence of SATD in the data access logic could seriously harm performance and maintainability. Understanding the composition and distribution of the SATDs across software systems and their evolution could provide insights into managing technical debt efficiently. We present a large-scale empirical study on the prevalence, composition, and evolution of SATD in data-intensive systems. We analyzed 83 open-source systems relying on relational databases as well as 19 systems relying on NoSQL databases. We detected SATD in source code comments obtained from different snapshots of the subject systems. To understand the evolution dynamics of SATDs, we conducted a survival analysis. Next, we performed a manual analysis of 361 sample data-access SATDs, investigating the composition of data-access SATDs and the reasons behind their introduction and removal. We identified 15 new SATD categories, out of which 11 are specific to database access operations. We found that most of the data-access SATDs are introduced in the later stages of change history rather than at the beginning. We also observed that bug fixing and refactoring are the main reasons behind the introduction of data-access SATDs.
ArticleNumber 130
Author Khomh, Foutse
Muse, Biruk Asmare
Cleve, Anthony
Nagy, Csaba
Antoniol, Giuliano
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Cites_doi 10.1007/978-3-030-62522-1_33
10.1145/1595808.1595817
10.1109/TSE.2017.2653105
10.1109/ICSME.2017.8
10.1007/s10664-020-09854-3
10.1109/TSE.2018.2831232
10.1287/mnsc.2015.2196
10.1109/SEAA.2018.00066
10.1109/TSE.2017.2654244
10.1109/MS.2012.130
10.1145/3379597.3387459
10.1109/MTD.2016.9
10.1145/3387906.3388630
10.1007/978-3-319-62386-3_14
10.1109/QRS.2016.38
10.1109/MTD.2014.17
10.1145/3196398.3196423
10.1016/j.infsof.2020.106257
10.1145/3183440.3183478
10.1109/MC.2010.227
10.1145/2684822.2685324
10.1109/MTD.2015.7332621
10.1109/ICSE.2015.59
10.1007/s11219-019-09442-9
10.1007/978-981-15-4851-2_28
10.1016/j.infsof.2018.05.010
10.1109/SANER48275.2020.9054868
10.1109/ICSME.2014.31
10.1109/MTD.2014.9
10.1109/MC.2008.125
10.1145/157709.157715
10.1007/s10664-017-9522-4
10.1145/3194164.3194170
10.1007/s10664-017-9540-2
10.1109/ICDE51399.2021.00008
10.1016/j.infsof.2015.10.008
10.1016/j.jss.2014.12.027
10.1016/j.jss.2019.02.056
10.1186/1471-2105-16-S13-S8
10.1145/2901739.2901742
10.1145/3236024.3264598
10.1109/SEAA.2019.00058
10.1080/01621459.1958.10501452
10.1145/3379597.3387467
10.1006/jcss.2000.1711
10.1145/3183440.3183496
10.1109/ICSME.2017.44
10.5281/zenodo.5825671
10.1109/SANER.2016.72
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Copyright The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
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Keywords Self-admitted technical debt
Data-intensive systems
Database access
Technical debt
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References Cunningham W (1992) The wycash portfolio management system. In: Addendum to the proceedings on object-oriented programming systems, languages, and applications (addendum), OOPSLA ’92. https://doi.org/10.1145/157709.157715. Association for Computing Machinery, pp 29–30
Weber JH, Cleve A, Meurice L, Ruiz FJB (2014) Managing technical debt in database schemas of critical software. In: 2014 Sixth international workshop on managing technical debt. https://doi.org/10.1109/MTD.2014.17, pp 43–46
Tufano M, Palomba F, Bavota G, Oliveto R, Di Penta M, De Lucia A, Poshyvanyk D (2015) When and why your code starts to smell bad. In: 2015 IEEE/ACM 37th IEEE international conference on software engineering, vol 1. IEEE, pp 403–414
PapadimitriouCHRaghavanPTamakiHVempalaSLatent semantic indexing: a probabilistic analysisJ Comput Syst Sci2000612217235180255610.1006/jcss.2000.1711
Zampetti F, Serebrenik A, Di Penta M (2020) Automatically learning patterns for self-admitted technical debt removal. In: 2020 IEEE 27th international conference on software analysis, evolution and reengineering (SANER). IEEE, pp 355–366
Potdar A, Shihab E (2014) An exploratory study on self-admitted technical debt. In: 2014 IEEE international conference on software maintenance and evolution. IEEE, pp 91–100
Al-Barak M, Bahsoon R (2016) Database design debts through examining schema evolution. In: 2016 IEEE 8th international workshop on managing technical debt (MTD). https://doi.org/10.1109/MTD.2016.9, pp 17–23
Sadalage PJ, Fowler M (2014) NoSQL distilled: a brief guide to the emerging world of polyglot persistence. Addison-Wesley
AlvesNSMendesTSde MendonçaMGSpínolaROShullFSeamanCIdentification and management of technical debtInf Softw Technol201670C10012110.1016/j.infsof.2015.10.008https://doi.org/10.1016/j.infsof.2015.10.008
Johannes D, Khomh F, Antoniol G (2019) A large-scale empirical study of code smells in javascript projects. Softw Qual J:1–44
Muse BA, Rahman MM, Nagy C, Cleve A, Khomh F, Antoniol G (2020) On the prevalence, impact, and evolution of sql code smells in data-intensive systems. In: Proceedings of the 17th international conference on mining software repositories, MSR ’20. https://doi.org/10.1145/3379597.3387467. Association for Computing Machinery, New York, pp 327–338
Yu Z, Fahid FM, Tu H, Menzies T (2020) Identifying self-admitted technical debts with jitterbug: a two-step approach. arXiv:2002.11049
HuangQShihabEXiaXLoDLiSIdentifying self-admitted technical debt in open source projects using text miningEmpir Softw Eng201823141845110.1007/s10664-017-9522-4
TufanoMPalombaFBavotaGOlivetoRDi PentaMDe LuciaAPoshyvanykDWhen and why your code starts to smell bad (and whether the smells go away)IEEE Trans Softw Eng201743111063108810.1109/TSE.2017.2653105
Kamei Y, Maldonado EDS, Shihab E, Ubayashi N (2016) Using analytics to quantify interest of self-admitted technical debt. In: QuASoq/TDA@ APSEC, pp 68–71
Lin D, Neamtiu I (2009) Collateral evolution of applications and databases. In: Proceedings of the joint international and annual ERCIM workshops on principles of software evolution (IWPSE) and software evolution (Evol) workshops. https://doi.org/10.1145/1595808.1595817. ACM, pp 31–40
BleiDMNgAYJordanMILatent dirichlet allocationJ Mach Learn Res2003399310221112.68379
Scherzinger S, Klettke M (2013) Managing schema evolution in noSQL data stores. In: Proceedings of the 14th international symposium on database programming languages (DBPL 2013)
YanMXiaXShihabELoDYinJYangXAutomating change-level self-admitted technical debt determinationIEEE Trans Softw Eng201845121211122910.1109/TSE.2018.2831232
Meurice L, Nagy C, Cleve A (2016) Detecting and preventing program inconsistencies under database schema evolution. In: Proceedings of the 2016 IEEE international conference on software quality, reliability and security (QRS 2016). https://doi.org/10.1109/QRS.2016.38. IEEE, pp 262–273
Chang J, Gerrish S, Wang C, Boyd-graber J, Blei D (2009) Reading tea leaves: how humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, Williams C, Culotta A (eds) Advances in neural information processing systems, vol 22. Curran Associates Inc
Röder M, Both A, Hinneburg A (2015) Exploring the space of topic coherence measures. In: Proceedings of the eighth ACM international conference on Web search and data mining, pp 399–408
Scherzinger S, Sidortschuck S (2020) An empirical study on the design and evolution of noSQL database schemas. In: Dobbie G, Frank U, Kappel G, Liddle SW, Mayr HC (eds) Conceptual modeling. Springer International Publishing, Cham, pp 441–455
De Freitas Farias MA, de Mendonça Neto MG, da Silva AB, Spínola RO (2015) A contextualized vocabulary model for identifying technical debt on code comments. In: 2015 IEEE 7th international workshop on managing technical debt (MTD). IEEE, pp 25–32
LimETaksandeNSeamanCA balancing act: what software practitioners have to say about technical debtIEEE Softw2012296222710.1109/MS.2012.130https://doi.org/10.1109/MS.2012.130
Xavier L, Ferreira F, Brito R, Valente MT (2020) Beyond the code: mining self-admitted technical debt in issue tracker systems. In: Proceedings of the 17th international conference on mining software repositories, MSR ’20. https://doi.org/10.1145/3379597.3387459. Association for Computing Machinery, pp 137–146
Stonebraker M, Deng D, Brodie ML (2017) Application-database co-evolution: a new design and development paradigm. In: New England database day
AnicheMBavotaGTreudeCGerosaMAvan DeursenACode smells for model-view-controller architecturesEmpir Softw Eng20182342121215710.1007/s10664-017-9540-2
CleveAMensTHainautJData-intensive system evolutionComputer201043811011210.1109/MC.2010.227https://doi.org/10.1109/MC.2010.227
SierraGShihabEKameiYA survey of self-admitted technical debtJ Syst Softw2019152708210.1016/j.jss.2019.02.056
da Silva MaldonadoEShihabETsantalisNUsing natural language processing to automatically detect self-admitted technical debtIEEE Trans Softw Eng201743111044106210.1109/TSE.2017.2654244
de Freitas Farias MA, Santos JA, Kalinowski M, Mendonça M, Spínola RO (2016) Investigating the identification of technical debt through code comment analysis. In: International conference on enterprise information systems. Springer, pp 284–309
Zampetti F, Serebrenik A, Di Penta M (2018) Was self-admitted technical debt removal a real removal? An in-depth perspective. In: Proceedings of the 15th international conference on mining software repositories, MSR ’18. https://doi.org/10.1145/3196398.3196423. Association for Computing Machinery, pp 526–536
Park B, Rao DL, Gudivada VN (2021) Dangers of bias in data-intensive information systems. In: Deshpande P, Abraham A, Iyer B, Ma K (eds) Next generation information processing system. Springer Singapore, Singapore, pp 259–271
Maldonado EDS, Abdalkareem R, Shihab E, Serebrenik A (2017) An empirical study on the removal of self-admitted technical debt. In: 2017 IEEE international conference on software maintenance and evolution (ICSME). IEEE, pp 238–248
Alfayez R, Alwehaibi W, Winn R, Venson E, Boehm B (2020) A systematic literature review of technical debt prioritization. In: Proceedings of the 3rd international conference on technical debt, TechDebt ’20. https://doi.org/10.1145/3387906.3388630. Association for Computing Machinery, pp 1–10
Vassiliadis P (2021) Profiles of schema evolution in free open source software projects. In: Proceedings of the 2021 IEEE 37th international conference on data engineering (ICDE), pp 1–12
Albarak M, Bahsoon R (2018) Prioritizing technical debt in database normalization using portfolio theory and data quality metrics. In: Proceedings of the 2018 international conference on technical debt, TechDebt ’18. https://doi.org/10.1145/3194164.3194170. Association for Computing Machinery, pp 31–40
GokhaleMCohenJYooAMillerWMJacobAUlmerCPearceRHardware technologies for high-performance data-intensive computingComputer2008414606810.1109/MC.2008.125
Bavota G, Russo B (2016) A large-scale empirical study on self-admitted technical debt. In: Proceedings of the 13th international conference on mining software repositories, pp 315–326
MaipraditRTreudeCHataHMatsumotoKWait for it: identifying “on-hold” self-admitted technical debtEmpir Softw Eng20202553770379810.1007/s10664-020-09854-3
Alves NSR, Ribeiro LF, Caires V, Mendes TS, Spíanol RO (2014) Towards an ontology of terms on technical debt. In: 2014 Sixth international workshop on managing technical debt. https://doi.org/10.1109/MTD.2014.9, pp 1–7
Nagy C, Cleve A (2018) SQLInspect: a static analyzer to inspect database usage in Java applications. In: Proceedings of the 40th international conference on software engineering: companion proceedings. ACM, pp 93–96
Zampetti F, Noiseux C, Antoniol G, Khomh F, Di Penta M (2017) Recommending when design technical debt should be self-admitted. In: 2017 IEEE International conference on software maintenance and evolution (ICSME). IEEE, pp 216–226
Wehaibi S, Shihab E, Guerrouj L (2016) Examining the impact of self-admitted technical debt on software quality. In: 2016 IEEE 23rd international conference on software analysis, evolution, and reengineering (SANER), vol 1. IEEE, pp 179–188
LiZAvgeriouPLiangPA systematic mapping study on technical debt and its managementJ Syst Softw2015101C19322010.1016/j.jss.2014.12.027https://doi.org/10.1016/j.jss.2014.12.027
RiosNde Mendonça NetoMGSpínolaROA tertiary study on technical debt: types, management strategies, research trends, and base information for practitionersInf Softw Technol201810211714510.1016/j.infsof.2018.05.010https://doi.org/10.1016/j.infsof.2018.05.010
Muse BA, Nagy C, Khomh F, Cleve A, Antoniol G (2022) Replication package for: FIXME: synchronize with database. An empirical study of data access self-admitted technical debt. https://doi.org/10.5281/zenodo.5825671
GitHub Inc (2019) Search API. https://developer.github.com/v3/search
Hummel O, Eichelberger H, Giloj A, Werle D, Schmid K (2018) A collection of software engineering ch
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10119_CR49
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M Aniche (10119_CR6) 2018; 23
10119_CR45
10119_CR47
10119_CR46
10119_CR41
10119_CR40
E da Silva Maldonado (10119_CR12) 2017; 43
10119_CR43
10119_CR42
NS Alves (10119_CR5) 2016; 70
DM Blei (10119_CR8) 2003; 3
E Lim (10119_CR25) 2012; 29
M Gokhale (10119_CR17) 2008; 41
G Sierra (10119_CR44) 2019; 152
10119_CR3
10119_CR2
M Yan (10119_CR53) 2018; 45
10119_CR1
10119_CR37
10119_CR34
10119_CR33
10119_CR36
10119_CR30
10119_CR32
W Zhao (10119_CR58) 2015; 16
Z Li (10119_CR24) 2015; 101
Q Huang (10119_CR18) 2018; 23
10119_CR9
10119_CR7
10119_CR4
10119_CR27
10119_CR26
10119_CR29
N Rios (10119_CR39) 2018; 102
A Cleve (10119_CR10) 2010; 43
10119_CR21
10119_CR20
10119_CR50
EL Kaplan (10119_CR22) 1958; 53
RGJr Miller (10119_CR31) 2011
10119_CR19
10119_CR16
10119_CR15
N Ramasubbu (10119_CR38) 2016; 62
M Kuutila (10119_CR23) 2020; 121
10119_CR56
10119_CR11
10119_CR55
10119_CR14
10119_CR13
10119_CR57
10119_CR52
10119_CR51
10119_CR54
References_xml – reference: Zampetti F, Noiseux C, Antoniol G, Khomh F, Di Penta M (2017) Recommending when design technical debt should be self-admitted. In: 2017 IEEE International conference on software maintenance and evolution (ICSME). IEEE, pp 216–226
– reference: PapadimitriouCHRaghavanPTamakiHVempalaSLatent semantic indexing: a probabilistic analysisJ Comput Syst Sci2000612217235180255610.1006/jcss.2000.1711
– reference: Sadalage PJ, Fowler M (2014) NoSQL distilled: a brief guide to the emerging world of polyglot persistence. Addison-Wesley
– reference: Weber JH, Cleve A, Meurice L, Ruiz FJB (2014) Managing technical debt in database schemas of critical software. In: 2014 Sixth international workshop on managing technical debt. https://doi.org/10.1109/MTD.2014.17, pp 43–46
– reference: LiZAvgeriouPLiangPA systematic mapping study on technical debt and its managementJ Syst Softw2015101C19322010.1016/j.jss.2014.12.027https://doi.org/10.1016/j.jss.2014.12.027
– reference: Röder M, Both A, Hinneburg A (2015) Exploring the space of topic coherence measures. In: Proceedings of the eighth ACM international conference on Web search and data mining, pp 399–408
– reference: MaipraditRTreudeCHataHMatsumotoKWait for it: identifying “on-hold” self-admitted technical debtEmpir Softw Eng20202553770379810.1007/s10664-020-09854-3
– reference: Hummel O, Eichelberger H, Giloj A, Werle D, Schmid K (2018) A collection of software engineering challenges for big data system development. In: 2018 44th Euromicro conference on software engineering and advanced applications (SEAA). https://doi.org/10.1109/SEAA.2018.00066, pp 362–369
– reference: Park B, Rao DL, Gudivada VN (2021) Dangers of bias in data-intensive information systems. In: Deshpande P, Abraham A, Iyer B, Ma K (eds) Next generation information processing system. Springer Singapore, Singapore, pp 259–271
– reference: Zampetti F, Serebrenik A, Di Penta M (2020) Automatically learning patterns for self-admitted technical debt removal. In: 2020 IEEE 27th international conference on software analysis, evolution and reengineering (SANER). IEEE, pp 355–366
– reference: HuangQShihabEXiaXLoDLiSIdentifying self-admitted technical debt in open source projects using text miningEmpir Softw Eng201823141845110.1007/s10664-017-9522-4
– reference: Scherzinger S, Sidortschuck S (2020) An empirical study on the design and evolution of noSQL database schemas. In: Dobbie G, Frank U, Kappel G, Liddle SW, Mayr HC (eds) Conceptual modeling. Springer International Publishing, Cham, pp 441–455
– reference: Chang J, Gerrish S, Wang C, Boyd-graber J, Blei D (2009) Reading tea leaves: how humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J, Williams C, Culotta A (eds) Advances in neural information processing systems, vol 22. Curran Associates Inc
– reference: Alves NSR, Ribeiro LF, Caires V, Mendes TS, Spíanol RO (2014) Towards an ontology of terms on technical debt. In: 2014 Sixth international workshop on managing technical debt. https://doi.org/10.1109/MTD.2014.9, pp 1–7
– reference: Albarak M, Bahsoon R (2018) Prioritizing technical debt in database normalization using portfolio theory and data quality metrics. In: Proceedings of the 2018 international conference on technical debt, TechDebt ’18. https://doi.org/10.1145/3194164.3194170. Association for Computing Machinery, pp 31–40
– reference: YanMXiaXShihabELoDYinJYangXAutomating change-level self-admitted technical debt determinationIEEE Trans Softw Eng201845121211122910.1109/TSE.2018.2831232
– reference: Spadini D, Aniche M, Bacchelli A (2018) Pydriller: Python framework for mining software repositories. In: Proceedings of the 2018 26th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering, ESEC/FSE 2018. https://doi.org/10.1145/3236024.3264598. Association for Computing Machinery, pp 908–911
– reference: KaplanELMeierPNonparametric estimation from incomplete observationsJ Am Stat Assocs1958532824574819386710.1080/01621459.1958.10501452
– reference: Meurice L, Nagy C, Cleve A (2016) Detecting and preventing program inconsistencies under database schema evolution. In: Proceedings of the 2016 IEEE international conference on software quality, reliability and security (QRS 2016). https://doi.org/10.1109/QRS.2016.38. IEEE, pp 262–273
– reference: Xavier L, Ferreira F, Brito R, Valente MT (2020) Beyond the code: mining self-admitted technical debt in issue tracker systems. In: Proceedings of the 17th international conference on mining software repositories, MSR ’20. https://doi.org/10.1145/3379597.3387459. Association for Computing Machinery, pp 137–146
– reference: GitHub Inc (2019) Search API. https://developer.github.com/v3/search/
– reference: Lin D, Neamtiu I (2009) Collateral evolution of applications and databases. In: Proceedings of the joint international and annual ERCIM workshops on principles of software evolution (IWPSE) and software evolution (Evol) workshops. https://doi.org/10.1145/1595808.1595817. ACM, pp 31–40
– reference: Scherzinger S, Klettke M (2013) Managing schema evolution in noSQL data stores. In: Proceedings of the 14th international symposium on database programming languages (DBPL 2013)
– reference: Wehaibi S, Shihab E, Guerrouj L (2016) Examining the impact of self-admitted technical debt on software quality. In: 2016 IEEE 23rd international conference on software analysis, evolution, and reengineering (SANER), vol 1. IEEE, pp 179–188
– reference: Alfayez R, Alwehaibi W, Winn R, Venson E, Boehm B (2020) A systematic literature review of technical debt prioritization. In: Proceedings of the 3rd international conference on technical debt, TechDebt ’20. https://doi.org/10.1145/3387906.3388630. Association for Computing Machinery, pp 1–10
– reference: Bavota G, Russo B (2016) A large-scale empirical study on self-admitted technical debt. In: Proceedings of the 13th international conference on mining software repositories, pp 315–326
– reference: Foidl H, Felderer M, Biffl S (2019) Technical debt in data-intensive software systems. In: 2019 45th Euromicro conference on software engineering and advanced applications (SEAA). https://doi.org/10.1109/SEAA.2019.00058, pp 338–341
– reference: Zampetti F, Serebrenik A, Di Penta M (2018) Was self-admitted technical debt removal a real removal? An in-depth perspective. In: Proceedings of the 15th international conference on mining software repositories, MSR ’18. https://doi.org/10.1145/3196398.3196423. Association for Computing Machinery, pp 526–536
– reference: da Silva MaldonadoEShihabETsantalisNUsing natural language processing to automatically detect self-admitted technical debtIEEE Trans Softw Eng201743111044106210.1109/TSE.2017.2654244
– reference: Al-Barak M, Bahsoon R (2016) Database design debts through examining schema evolution. In: 2016 IEEE 8th international workshop on managing technical debt (MTD). https://doi.org/10.1109/MTD.2016.9, pp 17–23
– reference: GokhaleMCohenJYooAMillerWMJacobAUlmerCPearceRHardware technologies for high-performance data-intensive computingComputer2008414606810.1109/MC.2008.125
– reference: Nagy C, Cleve A (2018) SQLInspect: a static analyzer to inspect database usage in Java applications. In: Proceedings of the 40th international conference on software engineering: companion proceedings. ACM, pp 93–96
– reference: LimETaksandeNSeamanCA balancing act: what software practitioners have to say about technical debtIEEE Softw2012296222710.1109/MS.2012.130https://doi.org/10.1109/MS.2012.130
– reference: MillerRGJrSurvival analysis2011New YorkWiley
– reference: Potdar A, Shihab E (2014) An exploratory study on self-admitted technical debt. In: 2014 IEEE international conference on software maintenance and evolution. IEEE, pp 91–100
– reference: Muse BA, Rahman MM, Nagy C, Cleve A, Khomh F, Antoniol G (2020) On the prevalence, impact, and evolution of sql code smells in data-intensive systems. In: Proceedings of the 17th international conference on mining software repositories, MSR ’20. https://doi.org/10.1145/3379597.3387467. Association for Computing Machinery, New York, pp 327–338
– reference: AnicheMBavotaGTreudeCGerosaMAvan DeursenACode smells for model-view-controller architecturesEmpir Softw Eng20182342121215710.1007/s10664-017-9540-2
– reference: Liu Z, Huang Q, Xia X, Shihab E, Lo D, Li S (2018) Satd detector: a text-mining-based self-admitted technical debt detection tool. In: Proceedings of the 40th international conference on software engineering: companion proceedings, pp 9–12
– reference: Maldonado EDS, Abdalkareem R, Shihab E, Serebrenik A (2017) An empirical study on the removal of self-admitted technical debt. In: 2017 IEEE international conference on software maintenance and evolution (ICSME). IEEE, pp 238–248
– reference: de Freitas Farias MA, Santos JA, Kalinowski M, Mendonça M, Spínola RO (2016) Investigating the identification of technical debt through code comment analysis. In: International conference on enterprise information systems. Springer, pp 284–309
– reference: Cunningham W (1992) The wycash portfolio management system. In: Addendum to the proceedings on object-oriented programming systems, languages, and applications (addendum), OOPSLA ’92. https://doi.org/10.1145/157709.157715. Association for Computing Machinery, pp 29–30
– reference: De Freitas Farias MA, de Mendonça Neto MG, da Silva AB, Spínola RO (2015) A contextualized vocabulary model for identifying technical debt on code comments. In: 2015 IEEE 7th international workshop on managing technical debt (MTD). IEEE, pp 25–32
– reference: Muse BA, Nagy C, Khomh F, Cleve A, Antoniol G (2022) Replication package for: FIXME: synchronize with database. An empirical study of data access self-admitted technical debt. https://doi.org/10.5281/zenodo.5825671
– reference: Johannes D, Khomh F, Antoniol G (2019) A large-scale empirical study of code smells in javascript projects. Softw Qual J:1–44
– reference: Stonebraker M, Deng D, Brodie ML (2017) Application-database co-evolution: a new design and development paradigm. In: New England database day
– reference: Tufano M, Palomba F, Bavota G, Oliveto R, Di Penta M, De Lucia A, Poshyvanyk D (2015) When and why your code starts to smell bad. In: 2015 IEEE/ACM 37th IEEE international conference on software engineering, vol 1. IEEE, pp 403–414
– reference: TufanoMPalombaFBavotaGOlivetoRDi PentaMDe LuciaAPoshyvanykDWhen and why your code starts to smell bad (and whether the smells go away)IEEE Trans Softw Eng201743111063108810.1109/TSE.2017.2653105
– reference: RiosNde Mendonça NetoMGSpínolaROA tertiary study on technical debt: types, management strategies, research trends, and base information for practitionersInf Softw Technol201810211714510.1016/j.infsof.2018.05.010https://doi.org/10.1016/j.infsof.2018.05.010
– reference: Vassiliadis P (2021) Profiles of schema evolution in free open source software projects. In: Proceedings of the 2021 IEEE 37th international conference on data engineering (ICDE), pp 1–12
– reference: AlvesNSMendesTSde MendonçaMGSpínolaROShullFSeamanCIdentification and management of technical debtInf Softw Technol201670C10012110.1016/j.infsof.2015.10.008https://doi.org/10.1016/j.infsof.2015.10.008
– reference: CleveAMensTHainautJData-intensive system evolutionComputer201043811011210.1109/MC.2010.227https://doi.org/10.1109/MC.2010.227
– reference: SierraGShihabEKameiYA survey of self-admitted technical debtJ Syst Softw2019152708210.1016/j.jss.2019.02.056
– reference: Kamei Y, Maldonado EDS, Shihab E, Ubayashi N (2016) Using analytics to quantify interest of self-admitted technical debt. In: QuASoq/TDA@ APSEC, pp 68–71
– reference: RamasubbuNKemererCFTechnical debt and the reliability of enterprise software systems: a competing risks analysisManag Sci20166251487151010.1287/mnsc.2015.2196https://doi.org/10.1287/mnsc.2015.2196
– reference: KuutilaMMäntyläMFarooqUClaesMTime pressure in software engineering: a systematic reviewInf Softw Technol202012110625710.1016/j.infsof.2020.106257https://doi.org/10.1016/j.infsof.2020.106257
– reference: Yu Z, Fahid FM, Tu H, Menzies T (2020) Identifying self-admitted technical debts with jitterbug: a two-step approach. arXiv:2002.11049
– reference: BleiDMNgAYJordanMILatent dirichlet allocationJ Mach Learn Res2003399310221112.68379
– reference: ZhaoWChenJJPerkinsRLiuZGeWDingYZouWA heuristic approach to determine an appropriate number of topics in topic modelingBMC Bioinform20151613S810.1186/1471-2105-16-S13-S8
– ident: 10119_CR43
  doi: 10.1007/978-3-030-62522-1_33
– ident: 10119_CR26
  doi: 10.1145/1595808.1595817
– volume: 43
  start-page: 1063
  issue: 11
  year: 2017
  ident: 10119_CR48
  publication-title: IEEE Trans Softw Eng
  doi: 10.1109/TSE.2017.2653105
– ident: 10119_CR29
  doi: 10.1109/ICSME.2017.8
– volume: 25
  start-page: 3770
  issue: 5
  year: 2020
  ident: 10119_CR28
  publication-title: Empir Softw Eng
  doi: 10.1007/s10664-020-09854-3
– volume: 45
  start-page: 1211
  issue: 12
  year: 2018
  ident: 10119_CR53
  publication-title: IEEE Trans Softw Eng
  doi: 10.1109/TSE.2018.2831232
– volume: 62
  start-page: 1487
  issue: 5
  year: 2016
  ident: 10119_CR38
  publication-title: Manag Sci
  doi: 10.1287/mnsc.2015.2196
– ident: 10119_CR19
  doi: 10.1109/SEAA.2018.00066
– volume: 43
  start-page: 1044
  issue: 11
  year: 2017
  ident: 10119_CR12
  publication-title: IEEE Trans Softw Eng
  doi: 10.1109/TSE.2017.2654244
– ident: 10119_CR41
– volume: 29
  start-page: 22
  issue: 6
  year: 2012
  ident: 10119_CR25
  publication-title: IEEE Softw
  doi: 10.1109/MS.2012.130
– ident: 10119_CR52
  doi: 10.1145/3379597.3387459
– ident: 10119_CR1
  doi: 10.1109/MTD.2016.9
– volume-title: Survival analysis
  year: 2011
  ident: 10119_CR31
– ident: 10119_CR3
  doi: 10.1145/3387906.3388630
– ident: 10119_CR14
  doi: 10.1007/978-3-319-62386-3_14
– ident: 10119_CR30
  doi: 10.1109/QRS.2016.38
– ident: 10119_CR16
– ident: 10119_CR50
  doi: 10.1109/MTD.2014.17
– ident: 10119_CR56
  doi: 10.1145/3196398.3196423
– volume: 121
  start-page: 106257
  year: 2020
  ident: 10119_CR23
  publication-title: Inf Softw Technol
  doi: 10.1016/j.infsof.2020.106257
– ident: 10119_CR27
  doi: 10.1145/3183440.3183478
– ident: 10119_CR54
– volume: 3
  start-page: 993
  year: 2003
  ident: 10119_CR8
  publication-title: J Mach Learn Res
– volume: 43
  start-page: 110
  issue: 8
  year: 2010
  ident: 10119_CR10
  publication-title: Computer
  doi: 10.1109/MC.2010.227
– ident: 10119_CR40
  doi: 10.1145/2684822.2685324
– ident: 10119_CR13
  doi: 10.1109/MTD.2015.7332621
– ident: 10119_CR47
  doi: 10.1109/ICSE.2015.59
– ident: 10119_CR20
  doi: 10.1007/s11219-019-09442-9
– ident: 10119_CR36
  doi: 10.1007/978-981-15-4851-2_28
– volume: 102
  start-page: 117
  year: 2018
  ident: 10119_CR39
  publication-title: Inf Softw Technol
  doi: 10.1016/j.infsof.2018.05.010
– ident: 10119_CR57
  doi: 10.1109/SANER48275.2020.9054868
– ident: 10119_CR37
  doi: 10.1109/ICSME.2014.31
– ident: 10119_CR4
  doi: 10.1109/MTD.2014.9
– ident: 10119_CR9
– volume: 41
  start-page: 60
  issue: 4
  year: 2008
  ident: 10119_CR17
  publication-title: Computer
  doi: 10.1109/MC.2008.125
– ident: 10119_CR11
  doi: 10.1145/157709.157715
– volume: 23
  start-page: 418
  issue: 1
  year: 2018
  ident: 10119_CR18
  publication-title: Empir Softw Eng
  doi: 10.1007/s10664-017-9522-4
– ident: 10119_CR2
  doi: 10.1145/3194164.3194170
– volume: 23
  start-page: 2121
  issue: 4
  year: 2018
  ident: 10119_CR6
  publication-title: Empir Softw Eng
  doi: 10.1007/s10664-017-9540-2
– ident: 10119_CR49
  doi: 10.1109/ICDE51399.2021.00008
– volume: 70
  start-page: 100
  issue: C
  year: 2016
  ident: 10119_CR5
  publication-title: Inf Softw Technol
  doi: 10.1016/j.infsof.2015.10.008
– volume: 101
  start-page: 193
  issue: C
  year: 2015
  ident: 10119_CR24
  publication-title: J Syst Softw
  doi: 10.1016/j.jss.2014.12.027
– volume: 152
  start-page: 70
  year: 2019
  ident: 10119_CR44
  publication-title: J Syst Softw
  doi: 10.1016/j.jss.2019.02.056
– volume: 16
  start-page: S8
  issue: 13
  year: 2015
  ident: 10119_CR58
  publication-title: BMC Bioinform
  doi: 10.1186/1471-2105-16-S13-S8
– ident: 10119_CR7
  doi: 10.1145/2901739.2901742
– ident: 10119_CR45
  doi: 10.1145/3236024.3264598
– ident: 10119_CR15
  doi: 10.1109/SEAA.2019.00058
– volume: 53
  start-page: 457
  issue: 282
  year: 1958
  ident: 10119_CR22
  publication-title: J Am Stat Assocs
  doi: 10.1080/01621459.1958.10501452
– ident: 10119_CR32
  doi: 10.1145/3379597.3387467
– ident: 10119_CR46
– ident: 10119_CR21
– volume: 61
  start-page: 217
  issue: 2
  year: 2000
  ident: 10119_CR35
  publication-title: J Comput Syst Sci
  doi: 10.1006/jcss.2000.1711
– ident: 10119_CR34
  doi: 10.1145/3183440.3183496
– ident: 10119_CR55
  doi: 10.1109/ICSME.2017.44
– ident: 10119_CR33
  doi: 10.5281/zenodo.5825671
– ident: 10119_CR42
– ident: 10119_CR51
  doi: 10.1109/SANER.2016.72
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Snippet Developers sometimes choose design and implementation shortcuts due to the pressure from tight release schedules. However, shortcuts introduce technical debt...
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SubjectTerms Compilers
Composition
Computer Science
Empirical analysis
Evolution
Interpreters
Maintainability
Programming Languages
Relational data bases
Software
Software development
Software engineering
Software Engineering/Programming and Operating Systems
Source code
Survival analysis
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Title FIXME: synchronize with database! An empirical study of data access self-admitted technical debt
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Volume 27
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