survey

Machine Learning for Software Engineering: A Tertiary Study

Authors:

Rafaila Galanopoulou,

Diomidis SpinellisAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 12

Article No.: 256, Pages 1 - 39

https://doi.org/10.1145/3572905

Published: 02 March 2023 Publication History

Abstract

Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009 and 2022, covering 6,117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions, including conducting further empirical validation and industrial studies on ML, reconsidering deficient SE methods, documenting and automating data collection and pipeline processes, reexamining how industrial practitioners distribute their proprietary data, and implementing incremental ML approaches.

References

[1]

Amjad AbuHassan, Mohammad Alshayeb, and Lahouari Ghouti. 2021. Software smell detection techniques: A systematic literature review. J. Softw.: Evol. Process 33, 3 (2021), e2320. DOI:

Digital Library

[2]

Arshad Ahmad, Chong Feng, Muzammil Khan, Asif Khan, Ayaz Ullah, Shah Nazir, Adnan Tahir, and Iqtadar Hussain. 2020. A systematic literature review on using machine learning algorithms for software requirements identification on stack overflow. Secur. Commun. Netw. 2020 (Jan.2020), 19. DOI:

Digital Library

[3]

Bestoun S. Ahmed, Kamal Z. Zamli, Wasif Afzal, and Miroslav Bures. 2017. Constrained interaction testing: A systematic literature study. IEEE Access 5 (2017), 25706–25730. DOI:

[4]

Ahmed Al-Shaaby, Hamoud Aljamaan, and Mohammad Alshayeb. 2020. Bad smell detection using machine learning techniques: A systematic literature review. Arab. J. Sci. Eng. 45, 4 (Jan.2020), 2341–2369. DOI:

[5]

Asad Ali and Carmine Gravino. 2019. A systematic literature review of software effort prediction using machine learning methods. J. Softw.: Evol. Process 31, 10 (2019), e2211. DOI:

Digital Library

[6]

Asad Ali and Carmine Gravino. 2019. Using bio-inspired features selection algorithms in software effort estimation: A systematic literature review. In Proceedings of the 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA’19). IEEE. DOI:

[7]

Asad Ali and Carmine Gravino. 2020. Bio-inspired algorithms in software fault prediction: A systematic literature review. In Proceedings of the 14th International Conference on Open Source Systems and Technologies (ICOSST’20). IEEE. DOI:

[8]

Nazakat Ali, Jang-Eui Hong, and Lawrence Chung. 2021. Social network sites and requirements engineering: A systematic literature review. J. Softw.: Evol. Process 33, 4 (2021), e2332. DOI:

Digital Library

[9]

Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, and Charles Sutton. 2018. A survey of machine learning for big code and naturalness. Comput. Surveys 51, 4, Article 81 (July2018), 37 pages. DOI:

Digital Library

[10]

Ahmed M. Alsalemi and Eng-Thiam Yeoh. 2018. A systematic literature review of requirements volatility prediction. In Proceedings of the International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC’17). IEEE, 55–64. DOI:

[11]

Hadeel Alsolai and Marc Roper. 2019. A systematic review of feature selection techniques in software quality prediction. Proceedings of the International Conference on Electrical and Computing Technologies and Applications. DOI:

[12]

Apostolos Ampatzoglou, Stamatia Bibi, Paris Avgeriou, Marijn Verbeek, and Alexander Chatzigeorgiou. 2019. Identifying, categorizing and mitigating threats to validity in software engineering secondary studies. Info. Softw. Technol. 106 (Feb.2019), 201–230. DOI:

[13]

Thazin Win Win Aung, Huan Huo, and Yulei Sui. 2020. A literature review of automatic traceability links recovery for software change impact analysis. In Proceedings of the 28th International Conference on Program Comprehension. ACM, New York, NY, 14–24. DOI:

Digital Library

[14]

Paris Avgeriou, Neil A. Ernst, Robert L. Nord, and Philippe Kruchten. 2016. Technical debt: Broadening perspectives report on the seventh workshop on managing technical debt. SIGSOFT Softw. Eng. Notes 41, 2 (May2016), 38–41. DOI:

Digital Library

[15]

Ahmet Aydin and Ken Anderson. 2017. Batch to real-time: Incremental data collection & analytics platform. In Proceedings of the 50th Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences. DOI:

[16]

Nathaniel Ayewah, William Pugh, J. David Morgenthaler, John Penix, and YuQian Zhou. 2007. Evaluating static analysis defect warnings on production software. In Proceedings of the 7th ACM SIGPLAN-SIGSOFT Workshop on Program Analysis for Software Tools and Engineering. ACM, 1–8. DOI:

[17]

Muhammad Ilyas Azeem, Fabio Palomba, Lin Shi, and Qing Wang. 2019. Machine learning techniques for code smell detection: A systematic literature review and meta-analysis. Info. Softw. Technol. 108 (Apr.2019), 115–138. DOI:

[18]

Mohammad Azzeh, Ali Bou Nassif, and Imtinan Basem Attili. 2021. Predicting software effort from use case points: A systematic review. Sci. Comput. Program. 204 (Apr.2021), 102596. DOI:

[19]

Ahmed Bahaa, Enas Mohamed Fathy, Ahmed Sharaf Eldin, Laila A. Abd-Elmegid, Ahmed Bahaa, and Ahmed Sharaf Eldin. 2021. A systematic literature review of software defect prediction using deep learning. J. Comput. Sci. 17, 5 (May2021), 490–510. DOI:

[20]

Noor H. Bakar, Zarinah M. Kasirun, and Norsaremah Salleh. 2015. Feature extraction approaches from natural language requirements for reuse in software product lines: A systematic literature review. J. Syst. Softw. 106, C (Aug.2015), 132–149. DOI:

[21]

Muneera Bano, Didar Zowghi, and Naveed Ikram. 2014. Systematic reviews in requirements engineering: A tertiary study. In Proceedings of the IEEE 4th International Workshop on Empirical Requirements Engineering (EmpiRE’14). IEEE. DOI:

[22]

Anahid Basiri. 2021. A novel model blah blah blah. J. Navigat. 74, 3 (2021), 501–504. DOI:

[23]

Iqra Batool and Tamim Ahmed Khan. 2022. Software fault prediction using data mining, machine learning and deep learning techniques: A systematic literature review. Comput. Electr. Eng. 100, C (May2022), 20. DOI:

[24]

Manuela Battaglia and Mark A. Atkinson. 2015. The streetlight effect in type 1 diabetes. Diabetes 64, 4 (2015), 1081–1090.

[25]

Markus Borg, Per Runeson, and Anders Ardö. 2014. Recovering from a decade: A systematic mapping of information retrieval approaches to software traceability. Empir. Softw. Eng. 19, 6 (Dec.2014), 1565–1616. DOI:

[26]

Pierre Bourque and Richard E. Fairley (Eds.). 2014. Guide to the Software Engineering Body of Knowledge, Version 3.0. IEEE Computer Society. Retrieved from www.swebok.org.

[27]

Pearl Brereton, Barbara A. Kitchenham, David Budgen, Mark Turner, and Mohamed Khalil. 2007. Lessons from applying the systematic literature review process within the software engineering domain. J. Syst. Softw. 80, 4 (2007), 571–583. DOI:

Digital Library

[28]

Frederick P. Brooks. 1987. No silver bullet: Essence and accidents of software engineering. Computer 20, 4 (Apr.1987), 10–19. DOI:

Digital Library

[29]

Frederick P. Brooks. 1995. The Mythical Man-Month (Anniversary Ed.). Addison-Wesley Longman Publishing.

Digital Library

[30]

Frederico Luiz Caram, Bruno Rafael De Oliveira Rodrigues, Amadeu Silveira Campanelli, and Fernando Silva Parreiras. 2019. Machine learning techniques for code smells detection: A systematic mapping study. Int. J. Softw. Eng. Knowl. Eng. 29, 02 (Feb.2019), 285–316. DOI:

[31]

Anita D. Carleton, Erin Harper, Tim Menzies, Tao Xie, Sigrid Eldh, and Michael R. Lyu. 2020. The AI effect: Working at the intersection of AI and SE. IEEE Softw. 37, 4 (2020), 26–35. DOI:

[32]

Alvaro Fernandez Del Carpio and Leonardo Bermon Angarita. 2020. Trends in software engineering processes using deep learning: A systematic literature review. In Proceedings of the 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA’20). IEEE. DOI:

[33]

Maria Caulo and Giuseppe Scanniello. 2020. A taxonomy of metrics for software fault prediction. In Proceedings of the 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA’20). IEEE. DOI:

[34]

Kathy Charmaz. 2014. Constructing Grounded Theory (2nd ed.). SAGE Publications.

[35]

Jaime Chavarriaga and Julio Ariel Hurtado. 2019. Second international workshop on experiences and empirical studies on software reuse (WEESR’19). In Proceedings of the 23rd International Systems and Software Product Line Conference—Volume A (SPLC’19). ACM, New York, NY, 321. DOI:

Digital Library

[36]

Tse-Hsun Chen, Stephen W. Thomas, and Ahmed E. Hassan. 2016. A survey on the use of topic models when mining software repositories. Empir. Softw. Eng. 21 (Oct.2016), 1843–1919. DOI:

Digital Library

[37]

Wontae Choi, George Necula, and Koushik Sen. 2013. Guided GUI testing of android apps with minimal restart and approximate learning. SIGPLAN Not. 48, 10 (Oct.2013), 623–640. DOI:

[38]

David A. Clifton, Jeremy Gibbons, Jim Davies, and Lionel Tarassenko. 2012. Machine learning and software engineering in health informatics. In Proceedings of the 1st International Workshop on Realizing AI Synergies in Software Engineering (RAISE’12). 37–41. DOI:

Digital Library

[39]

Juliet M. Corbin and Anselm Strauss. 1990. Grounded theory research: Procedures, canons, and evaluative criteria. Qual. Sociol. 13, 1 (1990), 3–21.

[40]

Christopher S. Corley, Kostadin Damevski, and Nicholas A. Kraft. 2015. Exploring the use of deep learning for feature location. In Proceedings of the IEEE International Conference on Software Maintenance and Evolution (ICSME’15). IEEE. DOI:

Digital Library

[41]

Dolors Costal, Carles Farré, Xavier Franch, and Carme Quer. 2021. How tertiary studies perform quality assessment of secondary studies in software engineering. In Proceedings of the 24th Iberoamerican Conference on Software Engineering (CIbSE’21). Curran Associates, 14.

[42]

R. A. Parker D. F. Williamson and J. S. Kendrick. 1989. The box plot: A simple visual method to interpret data. Ann. Intern. Med. 110, 11 (June1989), 916. DOI:

[43]

Fabio Q. B. da Silva, André L. M. Santos, Sérgio Soares, A. César C. França, Cleviton V. F. Monteiro, and Felipe Farias Maciel. 2011. Six years of systematic literature reviews in software engineering: An updated tertiary study. Info. Softw. Technol. 53, 9 (Sept.2011), 899–913. DOI:

Digital Library

[44]

M. del Carmen de Castro-Cabrera, Antonio García-Dominguez, and Inmaculada Medina-Bulo. 2020. Trends in prioritization of test cases: 2017–2019. In Proceedings of the 35th Annual ACM Symposium on Applied Computing (SAC’20). ACM, New York, NY, 2005–2011. DOI:

[45]

Isabel M. del Águila and José del Sagrado. 2015. Bayesian networks for enhancement of requirements engineering: A literature review. Require. Eng. 21, 4 (May2015), 461–480. DOI:

Digital Library

[46]

Liming Dong, Bohan Liu, Zheng Li, Ou Wu, Muhammad A. Babar, and Bingbing Xue. 2017. A mapping study on mining software process. In Proceedings of the 24th Asia-Pacific Software Engineering Conference (APSEC’17). IEEE, 51–60. DOI:

[47]

Alinne C. C. dos Santos, Ivaldir H. de Farias Junior, Hermano P. de Moura, and Sabrina Marczak. 2012. A systematic tertiary study of communication in distributed software development projects. In Proceedings of the IEEE 7th International Conference on Global Software Engineering. IEEE. DOI:

Digital Library

[48]

Vinicius H. S. Durelli, Rafael S. Durelli, Simone S. Borges, Andre T. Endo, Marcelo M. Eler, Diego R. C. Dias, and Marcelo P. Guimarães. 2019. Machine learning applied to software testing: A systematic mapping study. IEEE Trans. Reliabil. 68, 3 (Sept.2019), 1189–1212. DOI:

[49]

Tore Dybå and Torgeir Dingsøyr. 2008. Strength of evidence in systematic reviews in software engineering. In Proceedings of the 2nd International Symposium on Empirical Software Engineering and Measurement (ESEM’08). ACM, New York, NY, 178–187. DOI:

Digital Library

[50]

Steve Easterbrook, Janice Singer, Margaret-Anne Storey, and Daniela Damian. 2008. Selecting Empirical Methods for Software Engineering Research. Springer, London, 285–311. DOI:

[51]

Sara Elmidaoui, Laila Cheikhi, Ali Idri, and Alain Abran. 2019. Empirical studies on software product maintainability prediction: A systematic mapping and review. e-Info. Softw. Eng. J. 13, 1 (2019), 141–202. DOI:

[52]

Sara Elmidaoui, Laila Cheikhi, Ali Idri, and Alain Abran. 2020. Machine learning techniques for software maintainability prediction: Accuracy analysis. J. Comput. Sci. Technol. 35, 5 (Oct.2020), 1147–1174. DOI:

Digital Library

[53]

Sezen Erdem, Onur Demirörs, and Fethi Rabhi. 2018. Systematic mapping study on process mining in agile software development. In Proceedings of the 18th International Conference on Software Process Improvement and Capability Determination (SPICE’18). Springer International Publishing, 289–299. DOI:

[54]

Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, and Frank Hutter. 2019. Auto-sklearn: Efficient and robust automated machine learning. In Proceedings of the Automated Machine Learning. Springer International Publishing, 113–134. DOI:

[55]

Francesca Arcelli Fontana, Gilles Perrouin, Apostolos Ampatzoglou, Mathieu Archer, Bartosz Walter, Maxime Cordy, Fabio Palomba, and Xavier Devroey. 2020. MALTESQUE 2019 workshop summary. SIGSOFT Softw. Eng. Notes 45, 1 (Jan.2020), 34–35. DOI:

Digital Library

[56]

Chenchen Fu, Qiangqiang Liu, Peng Wu, Minming Li, Chun Jason Xue, Yingchao Zhao, Jingtong Hu, and Song Han. 2019. Real-time data retrieval in cyber-physical systems with temporal validity and data availability constraints. IEEE Trans. Knowl. Data Eng. 31, 9 (Sept.2019), 1779–1793. DOI:

Digital Library

[57]

Vahid Garousi and Mika V. Mäntylä. 2016. A systematic literature review of literature reviews in software testing. Info. Softw. Technol. 80, C (Dec.2016), 195–216. DOI:

Digital Library

[58]

Vahid Garousi, Kai Petersen, and Baris Ozkan. 2016. Challenges and best practices in industry-academia collaborations in software engineering: A systematic literature review. Info. Softw. Technol. 79 (2016), 106–127. DOI:

Digital Library

[59]

Lucian Gonçales, Kleinner Farias, Bruno da Silva, and Jonathan Fessler. 2019. Measuring the cognitive load of software developers: A systematic mapping study. In Proceedings of the 27th IEEE/ACM International Conference on Program Comprehension (ICPC’19). IEEE, 42–52. DOI:

Digital Library

[60]

Lucian José Gonçales, Kleinner Farias, and Bruno C. da Silva. 2021. Measuring the cognitive load of software developers: An extended systematic mapping study. Info. Softw. Technol. 136, C (Aug.2021), 30. DOI:

[61]

Xiaodong Gu, Hongyu Zhang, and Sunghun Kim. 2018. Deep code search. In Proceedings of the 40th International Conference on Software Engineering (ICSE’18). ACM, 933–944. DOI:

Digital Library

[62]

Tracy Hall, Sarah Beecham, David Bowes, David Gray, and Steve Counsell. 2012. A systematic literature review on fault prediction performance in software engineering. IEEE Trans. Softw. Eng. 38 (2012), 1276–1304. DOI:

Digital Library

[63]

Geir K. Hanssen, Darja Šmite, and Nils Brede Moe. 2011. Signs of agile trends in global software engineering research: A tertiary study. In Proceedings of the 6th International Conference on Global Software Engineering Workshop (ICGSE-W’11). IEEE Computer Society, 17–23. DOI:

Digital Library

[64]

Tom E. Hardwicke and John P. A. Ioannidis. 2018. Mapping the universe of registered reports. Nature Hum. Behav. 2, 11 (2018), 793–796.

[65]

Mark Harman. 2012. The role of artificial intelligence in software engineering. In Proceedings of the 1st International Workshop on Realizing AI Synergies in Software Engineering (RAISE’12). 1–6. DOI:

Digital Library

[66]

Ruben Heradio, David Fernandez-Amoros, Cristina Cerrada, and Manuel Cobo. 2021. Machine learning for software engineering: A bibliometric analysis from 2015 to 2019. DOI:

[67]

Seyedrebvar Hosseini, Burak Turhan, and Dimuthu Gunarathna. 2019. A systematic literature review and meta-analysis on cross project defect prediction. IEEE Trans. Softw. Eng. 45, 2 (Feb.2019), 111–147. DOI:

[68]

K. E. Huff and O. G Selfridge. 1990. Evolution in future intelligent information systems. In Proceedings of the International Workshop on the Development of Intelligent Information Systems.

[69]

Ali Idri, Ibtissam Abnane, and Alain Abran. 2015. Systematic mapping study of missing values techniques in software engineering data. In Proceedings of the 16th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD’15). IEEE, 1–8. DOI:

[70]

Ali Idri, Mohamed Hosni, and Alain Abran. 2016. Systematic literature review of ensemble effort estimation. J. Syst. Softw. 118, C (Aug.2016), 151–175. DOI:

[71]

Ali Idri, Mohamed Hosni, and Alain Abran. 2016. Systematic mapping study of ensemble effort estimation. In Proceedings of the 11th International Conference on Evaluation of Novel Software Approaches to Software Engineering (ENASE’16). 132–139. DOI:

Digital Library

[72]

IEEE-CS Professional & Educational Activities Board (PEAB) SWEBOK Evolution Team. 2022. IEEE-CS SWEBOK V4 Public Review. Retrieved from https://www.computer.org/volunteering/boards-and-committees/professional-educational-activities/software-engineering-committee/swebok-evolution.Accessed November 2022.

[73]

Salma Imtiaz, Muneera Bano, Naveed Ikram, and Mahmood Niazi. 2013. A tertiary study: Experiences of conducting systematic literature reviews in software engineering. In Proceedings of the 17th International Conference on Evaluation and Assessment in Software Engineering (EASE’13). ACM, New York, NY, 177–182. DOI:

Digital Library

[74]

Darrel C. Ince, Leslie Hatton, and John Graham-Cumming. 2012. The case for open computer programs. Nature 482, 7386 (2012), 485–488.

[75]

Gaeul Jeong, Sunghun Kim, and Thomas Zimmermann. 2009. Improving bug triage with bug tossing graphs. In Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on The Foundations of Software Engineering (ESEC/FSE’09). ACM, New York, NY, 111–120. DOI:

Digital Library

[76]

Magne Jørgensen and Martin Shepperd. 2007. A systematic review of software development cost estimation studies. IEEE Trans. Softw. Eng. 33 (Feb.2007), 33–53. DOI:

[77]

Arvinder Kaur and Shubhra Goyal Jindal. 2018. Severity prediction of bug reports using text mining: A systematic review. In Proceedings of the International Conference on Advances in Computing, Communication Control and Networking (ICACCCN’18). IEEE. DOI:

[78]

Rafaqut Kazmi, Dayang N. A. Jawawi, Radziah Mohamad, and Imran Ghani. 2017. Effective regression test case selection: A systematic literature review. Comput. Surveys 50, 2 (June2017), 32. DOI:

Digital Library

[79]

Muhammad Khatibsyarbini, Mohd Adham Isa, Dayang N. A. Jawawi, Muhammad Luqman Mohd Shafie, Wan Mohd Nasir Wan-Kadir, Haza Nuzly Abdull Hamed, and Muhammad Dhiauddin Mohamed Suffian. 2021. Trend application of machine learning in test case prioritization: A review on techniques. IEEE Access 9 (2021), 166262–166282. DOI:

[80]

Ayesha Kiran, Wasi H. Butt, Muhammad W. Anwar, Farooque Azam, and Bilal Maqbool. 2019. A comprehensive investigation of modern test suite optimization trends, tools and techniques. IEEE Access 7 (2019), 89093–89117. DOI:

[81]

Barbara Kitchenham. 2004. Procedures for performing systematic reviews. Keele University Technical Report TR/SE-0401, Keele, UK.

[82]

Barbara Kitchenham and Pearl Brereton. 2013. A systematic review of systematic review process research in software engineering. Info. Softw. Technol. 55, 12 (Dec.2013), 2049–2075. DOI:

Digital Library

[83]

Barbara Kitchenham and Stuart Charters. 2007. Guidelines for performing systematic literature reviews in software engineering. EBSE Technical Report EBSE-2007-01.

[84]

Barbara Kitchenham, O. Pearl Brereton, David Budgen, Mark Turner, John Bailey, and Stephen Linkman. 2009. Systematic literature reviews in software engineering—A systematic literature review. Info. Softw. Technol. 51, 1 (Jan.2009), 7–15. DOI:

[85]

Barbara Kitchenham, Rialette Pretorius, David Budgen, O. Pearl Brereton, Mark Turner, Mahmood Niazi, and Stephen Linkman. 2010. Systematic literature reviews in software engineering—A tertiary study. Info. Softw. Technol. 52, 8 (Aug.2010), 792–805. DOI:

Digital Library

[86]

Barbara Ann Kitchenham, David Budgen, and Pearl Brereton. 2015. Evidence-based Software Engineering and Systematic Reviews. Chapman & Hall/CRC.

[87]

Barbara A. Kitchenham, David Budgen, and O. Pearl Brereton. 2011. Using mapping studies as the basis for further research—A participant-observer case study. Info. Softw. Technol. 53, 6 (June2011), 638–651. DOI:

Digital Library

[88]

Zoe Kotti, Konstantinos Kravvaritis, Konstantina Dritsa, and Diomidis Spinellis. 2020. Standing on shoulders or feet? An extended study on the usage of the MSR data papers. Empir. Softw. Eng. 25 (July2020), 3288–3322. DOI:

Digital Library

[89]

Salma E. Koutbi, Ali Idri, and Alain Abran. 2016. Systematic mapping study of dealing with error in software development effort estimation. In Proceedings of the 42th Euromicro Conference on Software Engineering and Advanced Applications (SEAA’16). IEEE, 140–147. DOI:

[90]

Klaus Krippendorff. 2018. Content Analysis: An Introduction to Its Methodology (4th ed.). SAGE Publications.

[91]

Triet H. M. Le, Hao Chen, and Muhammad Ali Babar. 2020. Deep learning for source code modeling and generation: Models, applications, and challenges. Comput. Surveys 53, 3, Article 62 (June2020), 38 pages. DOI:

[92]

Yuxiang Lei and Yulei Sui. 2019. Fast and precise handling of positive weight cycles for field-sensitive pointer analysis. In Proceedings of the 26th International Symposium on Static Analysis. Springer-Verlag, Berlin, 27–47. DOI:

Digital Library

[93]

Tomasz Lewowski and Lech Madeyski. 2022. Code Smells Detection Using Artificial Intelligence Techniques: A Business-Driven Systematic Review. Springer International Publishing, Cham, 285–319. DOI:

[94]

Guangjie Li, Hui Liu, and Ally S. Nyamawe. 2020. A survey on renamings of software entities. Comput. Surveys 53 (April2020). DOI:

[95]

Ming Li, Hongyu Zhang, David Lo, and Lucia. 2015. Improving software quality and productivity leveraging mining techniques: [Summary of the second workshop on software mining at ASE 2013]. SIGSOFT Softw. Eng. Notes 40, 1 (Feb.2015), 1–2. DOI:

Digital Library

[96]

Yang Li, Sandro Schulze, and Gunter Saake. 2017. Reverse engineering variability from natural language documents: A systematic literature review. In Proceedings of the 21st International Systems and Software Product Line Conference—Volume A (SPLC’17). ACM, 133–142. DOI:

Digital Library

[97]

Johan Linåker, Sardar Muhammad Sulaman, Rafael Maiani de Mello, and Martin Höst. 2015. Guidelines for Conducting Surveys in Software Engineering. Department of Computer Science, Lund University.

[98]

Yasir Mahmood, Nazri Kama, Azri Azmi, Ahmad Salman Khan, and Mazlan Ali. 2022. Software effort estimation accuracy prediction of machine learning techniques: A systematic performance evaluation. Softw.: Pract. Exper. 52, 1 (2022), 39–65. DOI:

[99]

Ruchika Malhotra. 2015. A systematic review of machine learning techniques for software fault prediction. Appl. Soft Comput. 27 (Feb.2015), 504–518. DOI:

Digital Library

[100]

Ruchika Malhotra and Ankita Bansal. 2015. Predicting change using software metrics: A review. In Proceedings of the 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO’15). IEEE, 1–6. DOI:

[101]

Ruchika Malhotra and Ankita J. Bansal. 2016. Software change prediction: A literature review. Int. J. Comput. Appl. Technol. 54 (Jan.2016), 240–256. DOI:

Digital Library

[102]

Ruchika Malhotra and Anuradha Chug. 2016. Software maintainability: Systematic literature review and current trends. Int. J. Softw. Eng. Knowl. Eng. 26, 08 (Oct.2016), 1221–1253. DOI:

[103]

Ruchika Malhotra and Megha Khanna. 2019. Software change prediction: A systematic review and future guidelines. e-Informat. Softw. Eng. J. 13, 1 (2019), 227–259. DOI:

[104]

Ruchika Malhotra, Megha Khanna, and Rajeev R. Raje. 2017. On the application of search-based techniques for software engineering predictive modeling: A systematic review and future directions. Swarm Evolution. Comput. 32 (Feb.2017), 85–109. DOI:

[105]

Ruchika Malhotra and Kusum Lata. 2020. A systematic literature review on empirical studies towards prediction of software maintainability. Soft Comput. 24, 21 (May2020), 16655–16677. DOI:

Digital Library

[106]

C. Marimuthu and K. Chandrasekaran. 2017. Systematic studies in software product lines: A tertiary study. In Proceedings of the 21st International Systems and Software Product Line Conference—Volume A (SPLC’17). ACM, New York, NY, 143–152. DOI:

Digital Library

[107]

Anna Beatriz Marques, Rosiane Rodrigues, and Tayana Conte. 2012. Systematic literature reviews in distributed software development: A tertiary study. In Proceedings of the IEEE Seventh International Conference on Global Software Engineering. 134–143. DOI:

Digital Library

[108]

Alberto Martín-Martín, Enrique Orduna-Malea, Mike Thelwall, and Emilio Delgado López-Cózar. 2018. Google scholar, web of science, and scopus: A systematic comparison of citations in 252 subject categories. J. Informetr. 12, 4 (Nov.2018), 1160–1177. DOI:

[109]

Silverio Martínez-Fernández, Justus Bogner, Xavier Franch, Marc Oriol, Julien Siebert, Adam Trendowicz, Anna Maria Vollmer, and Stefan Wagner. 2022. Software engineering for AI-based systems: A survey. ACM Trans. Softw. Eng. Methodol. 31, 2, Article 37e (April2022), 59 pages. DOI:

Digital Library

[110]

Spyridon Mastorakis, Peter Gusev, Alexander Afanasyev, and Lixia Zhang. 2018. Real-time data retrieval in named data networking. In Proceedings of the 1st IEEE International Conference on Hot Information-Centric Networking (HotICN’18). IEEE. DOI:

[111]

Faseeha Matloob, Shabib Aftab, Munir Ahmad, Muhammad Adnan Khan, Areej Fatima, Muhammad Iqbal, Wesam Mohsen Alruwaili, and Nouh Sabri Elmitwally. 2021. Software defect prediction using supervised machine learning techniques: A systematic literature review. Intell. Autom. Soft Comput. 29, 2 (2021), 403–421. DOI:

[112]

Dean Richard McKinnel, Tooska Dargahi, Ali Dehghantanha, and Kim-Kwang Raymond Choo. 2019. A systematic literature review and meta-analysis on artificial intelligence in penetration testing and vulnerability assessment. Comput. Electr. Eng. 75, C (May2019), 175–188. DOI:

Digital Library

[113]

Karl Meinke and Amel Bennaceur. 2018. Machine learning for software engineering: Models, methods, and applications. In Proceedings of the 40th International Conference on Software Engineering: Companion Proceedings (ICSE’18). ACM, New York, NY, 548–549. DOI:

Digital Library

[114]

Assia Najm, Abdelali Zakrani, and Abdelaziz Marzak. 2019. Decision trees-based software development effort estimation: A systematic mapping study. Proceedings of the 2nd International Conference of Computer Science and Renewable Energies. DOI:

[115]

Dinithi Nallaperuma, Rashmika Nawaratne, Tharindu Bandaragoda, Achini Adikari, Su Nguyen, Thimal Kempitiya, Daswin De Silva, Damminda Alahakoon, and Dakshan Pothuhera. 2019. Online incremental machine learning platform for big data-driven smart traffic management. IEEE Trans. Intell. Transport. Syst. 20, 12 (Dec.2019), 4679–4690. DOI:

Digital Library

[116]

Marcus Norberto, Lukas Gaedicke, Maicon Bernardino, Guilherme Legramante, Fabio Paulo Basso, and Elder Macedo Rodrigues. 2019. Performance testing in mobile application: A systematic literature map. In Proceedings of the 28th Brazilian Symposium on Software Quality (SBQS’19). ACM, New York, NY, 99–108. DOI:

Digital Library

[117]

The Joint Task Force on Computing Curricula. 2004. Curriculum Guidelines for Undergraduate Degree Programs in Software Engineering. Technical Report. New York, NY, USA. DOI:

[118]

Pablo F. Ordoñez Ordoñez, Milton Quizhpe, Oscar M. Cumbicus-Pineda, Valeria Herrera Salazar, and Roberth Figueroa-Diaz. 2018. Application of genetic algorithms in software engineering: A systematic literature review. In Proceedings of the 4th International Conference on Technology Trends (CITT’18). Springer International Publishing, 659–670. DOI:

[119]

R. Özakinc and A. Tarhan. 2016. Yazilim gelistirmede erken asamalarda toplanan verinin hata tahmini performansina etkisi. In Proceedings of the 10th Turkish National Software Engineering Symposium (UYMS’16). CEUR-WS, 532–543.

[120]

Rana Özakıncı and Ayça Tarhan. 2018. Early software defect prediction: A systematic map and review. J. Syst. Softw. 144 (Oct.2018), 216–239. DOI:

Digital Library

[121]

Jalaj Pachouly, Swati Ahirrao, Ketan Kotecha, Ganeshsree Selvachandran, and Ajith Abraham. 2022. A systematic literature review on software defect prediction using artificial intelligence: Datasets, data validation methods, approaches, and tools. Eng. Appl. Artific. Intell. 111 (May2022), 104773. DOI:

[122]

Sushant Kumar Pandey, Ravi Bhushan Mishra, and Anil Kumar Tripathi. 2021. Machine learning based methods for software fault prediction: A survey. Expert Syst. Appl. 172 (June2021), 114595. DOI:

[123]

Juliana Alves Pereira, Mathieu Acher, Hugo Martin, Jean-Marc Jézéquel, Goetz Botterweck, and Anthony Ventresque. 2021. Learning software configuration spaces: A systematic literature review. J. Syst. Softw. 182, C (Dec.2021), 29. DOI:

[124]

Jorge Pérez, Jessica Díaz, Javier Garcia-Martin, and Bernardo Tabuenca. 2020. Systematic literature reviews in software engineering-enhancement of the study selection process using cohen’s kappa statistic. J. Syst. Softw. (2020), 110657.

[125]

Mirko Perkusich, Lenardo Chaves e Silva, Alexandre Costa, Felipe Ramos, Renata Saraiva, Arthur Freire, Ednaldo Dilorenzo, Emanuel Dantas, Danilo Santos, Kyller Gorgônio, Kyller Almeida, and Angelo Perkusich. 2020. Intelligent software engineering in the context of agile software development: A systematic literature review. Info. Softw. Technol. 119 (Mar.2020). DOI:

[126]

Kai Petersen, Robert Feldt, Shahid Mujtaba, and Michael Mattsson. 2008. Systematic mapping studies in software engineering. In Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering (EASE’08). BCS Learning & Development, Swindon, GBR, 68–77.

[127]

Kai Petersen, Sairam Vakkalanka, and Ludwik Kuzniarz. 2015. Guidelines for conducting systematic mapping studies in software engineering: An update. Info. Softw. Technol. 64 (2015), 1–18. DOI:

Digital Library

[128]

Vasileios C. Pezoulas, Konstantina D. Kourou, Fanis Kalatzis, Themis P. Exarchos, Evi Zampeli, Saviana Gandolfo, Andreas Goules, Chiara Baldini, Fotini Skopouli, Salvatore De Vita, Athanasios G. Tzioufas, and Dimitrios I. Fotiadis. 2020. Overcoming the barriers that obscure the interlinking and analysis of clinical data through harmonization and incremental learning. IEEE Open J. Eng. Med. Biol. 1 (2020), 83–90. DOI:

[129]

Henning Piezunka and Linus Dahlander. 2015. Distant search, narrow attention: How crowding alters organizations’ filtering of suggestions in crowdsourcing. Acad. Manage. J. 58, 3 (2015), 856–880. DOI:

[130]

Sreekumar P. Pillai, S. D. Madhukumar, and T. Radharamanan. 2017. Consolidating evidence based studies in software cost/effort estimation—A tertiary study. In Proceedings of the TENCON IEEE Region 10 Conference. 833–838. DOI:

[131]

Critical Appraisal Skills Programme. 2022. CASP Systematic Review Checklist. Retrieved from https://casp-uk.net/casp-tools-checklists/.Accessed July 2022.

[132]

Alexandre Quemy. 2019. Data pipeline selection and optimization. In Proceedings of the 21st International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP’19), Vol. 2324. CEUR-WS.org.

[133]

Lukasz Radlinski. 2010. A Survey of Bayesian Net Models for Software Development Effort Prediction. Int. J. Softw. Eng. Comput. 2, 2 (July2010), 95–109.

[134]

Ani Rahmani, Sabrina Ahmad, Intan Ermahani A. Jalil, and Adhitia Putra Herawan. 2021. A systematic literature review on regression test case prioritization. Int. J. Adv. Comput. Sci. Appl. 12, 9 (2021). DOI:

[135]

Saif U. Rehman Khan, Sai P. Lee, Nadeem Javaid, and Wadood Abdul. 2018. A systematic review on test suite reduction: Approaches, experiment’s quality evaluation, and guidelines. IEEE Access 6 (Feb.2018), 11816–11841. DOI:

[136]

Mehwish Riaz, Emilia Mendes, and Ewan Tempero. 2009. A systematic review of software maintainability prediction and metrics. In Proceedings of the 3rd International Symposium on Empirical Software Engineering and Measurement (ESEM’09). IEEE, 367–377. DOI:

Digital Library

[137]

Gregorio Robles, Laura Arjona Reina, Alexander Serebrenik, Bogdan Vasilescu, and Jesús M. González-Barahona. 2014. FLOSS 2013: A survey dataset about free software contributors: Challenges for curating, sharing, and combining. In Proceedings of the 11th Working Conference on Mining Software Repositories (MSR’14). ACM, New York, NY, 396–399. DOI:

Digital Library

[138]

Bernard Rous. 2012. Major update to ACM’s computing classification system. Commun. ACM 55, 11 (Nov.2012), 12. DOI:

[139]

Bushra Sabir, Faheem Ullah, M. Ali Babar, and Raj Gaire. 2021. Machine learning for detecting data exfiltration: A review. Comput. Surveys 54, 3, Article 50 (May2021), 47 pages. DOI:

[140]

Fatima Sabir, Francis Palma, Ghulam Rasool, Yann-Gaël Guéhéneuc, and Naouel Moha. 2019. A systematic literature review on the detection of smells and their evolution in object-oriented and service-oriented systems. Softw.: Pract. Exper. 49, 1 (2019), 3–39. DOI:

[141]

Zaineb Sakhrawi, Asma Sellami, and Nadia Bouassida. 2021. Software enhancement effort prediction using machine-learning techniques: A systematic mapping study. SN Comput. Sci. 2, 6 (Sept.2021). DOI:

[142]

Oliver G. Selfridge. 1993. The gardens of learning: A vision for AI. AI Mag. 14, 2 (Mar.1993). DOI:

[143]

Abubakar Omari Abdallah Semasaba, Wei Zheng, Xiaoxue Wu, and Samuel Akwasi Agyemang. 2020. Literature survey of deep learning-based vulnerability analysis on source code. IET Softw. 14, 6 (Dec.2020), 654–664. DOI:

[144]

Tushar Sharma and Diomidis Spinellis. 2018. A survey on software smells. J. Syst. Softw. 138 (2018), 158–173. DOI:

[145]

Camila Costa Silva, Matthias Galster, and Fabian Gilson. 2021. Topic modeling in software engineering research. Empir. Softw. Eng. 26, 6 (Nov.2021), 62. DOI:

Digital Library

[146]

Hazrina Sofian, Nur Arzilawati Md Yunus, and Rodina Ahmad. 2022. Systematic mapping: Artificial intelligence techniques in software engineering. IEEE Access 10 (2022), 51021–51040. DOI:

[147]

Md. Fahimuzzman Sohan and Anas Basalamah. 2020. A systematic literature review and quality analysis of javascript malware detection. IEEE Access 8 (2020), 190539–190552. DOI:

[148]

Le Son, Nakul Pritam, Manju Khari, Raghvendra Kumar, Pham Phuong, and Pham Thong. 2019. Empirical study of software defect prediction: A systematic mapping. Symmetry 11, 2 (Feb.2019), 212. DOI:

[149]

Yulei Sui and Jingling Xue. 2016. SVF: Interprocedural static value-flow analysis in LLVM. In Proceedings of the 25th International Conference on Compiler Construction. ACM, New York, NY, 265–266. DOI:

Digital Library

[150]

Xiaobing Sun, Xiangyue Liu, Bin Li, Yucong Duan, Hui Yang, and Jiajun Hu. 2016. Exploring topic models in software engineering data analysis: A survey. Proceedings of the 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. 357–362. DOI:

[151]

Sundaram Suresh, Sriram Narasimhan, Satish Nagarajaiah, and Narasimhan Sundararajan. 2010. Fault-tolerant adaptive control of nonlinear base-isolated buildings using EMRAN. Eng. Struct. 32, 8 (Aug.2010), 2477–2487. DOI:

[152]

Csaba Szepesvári. 2010. Algorithms for Reinforcement Learning. Morgan & Claypool Publishers. DOI:

[153]

M. Irtaza N. Tarar, Mubashir Ali, and Wasi H. Butt. 2019. Bug report summarization: A systematic literature review. In Proceedings of the 11th International Conference on Education Technology and Computers (ICETC’19). ACM, New York, NY, 257–261. DOI:

Digital Library

[154]

Chris Thornton, Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. 2013. Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’13). ACM, New York, NY, 847–855. DOI:

Digital Library

[155]

Kashyap Todi, Jean Vanderdonckt, Xiaojuan Ma, Jeffrey Nichols, and Nikola Banovic. 2020. AI4AUI: Workshop on AI methods for adaptive user interfaces. In Proceedings of the 25th International Conference on Intelligent User Interfaces Companion (IUI’20). ACM, New York, NY, 17–18. DOI:

Digital Library

[156]

Ayse Tosun, Ayse B. Bener, and Shirin Akbarinasaji. 2017. A systematic literature review on the applications of bayesian networks to predict software quality. Softw. Qual. J. 25 (Mar.2017), 273–305. DOI:

[157]

Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk. 2018. An empirical investigation into learning bug-fixing patches in the wild via neural machine translation. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (ASE’18). ACM, 832–837. DOI:

Digital Library

[158]

Jamal Uddin, Rozaida Ghazali, Mustafa M. Deris, Rashid Naseem, and Habib Shah. 2017. A survey on bug prioritization. Artific. Intell. Rev. 47, 2 (Feb.2017), 145–180. DOI:

Digital Library

[159]

Faheem Ullah, Matthew Edwards, Rajiv Ramdhany, Ruzanna Chitchyan, M. Ali Babar, and Awais Rashid. 2018. Data exfiltration: A review of external attack vectors and countermeasures. J. Netw. Comput. Appl. 101 (Jan.2018), 18–54. DOI:

Digital Library

[160]

Muhammad Usman, Ricardo Britto, Jürgen Börstler, and Emilia Mendes. 2017. Taxonomies in software engineering: A systematic mapping study and a revised taxonomy development method. Info. Softw. Technol. 85 (2017), 43–59. DOI:

Digital Library

[161]

B. F. van Dongen, A. K. A. de Medeiros, H. M. W. Verbeek, A. J. M. M. Weijters, and W. M. P. van der Aalst. 2005. The ProM framework: A new era in process mining tool support. In Proceedings of the International Conference on Application and Theory of Petri Nets (ICATPN’05), Gianfranco Ciardo and Philippe Darondeau (Eds.). Springer, Berlin, 444–454. DOI:

Digital Library

[162]

June M. Verner, O. Pearl Brereton, Barbara A. Kitchenham, Mark Turner, and Mahmood Niazi. 2012. Systematic literature reviews in global software development: A tertiary study. In Proceedings of the 16th International Conference on Evaluation Assessment in Software Engineering (EASE’12). 2–11. DOI:

[163]

Hai Vu-Ngoc, Sameh Samir Elawady, Ghaleb Muhammad Mehyar, Amr Hesham Abdelhamid, Omar Mohamed Mattar, Oday Halhouli, Nguyen Lam Vuong, Citra Dewi Mohd Ali, Ummu Helma Hassan, Nguyen Dang Kien, Kenji Hirayama, and Nguyen Tien Huy. 2018. Quality of flow diagram in systematic review and/or meta-analysis. PLoS One 13, 6 (June2018), 1–16. DOI:

[164]

Cody Watson, Nathan Cooper, David Nader Palacio, Kevin Moran, and Denys Poshyvanyk. 2022. A systematic literature review on the use of deep learning in software engineering research. ACM Trans. Softw. Eng. Methodol. 31, 2, Article 32 (March2022), 58 pages. DOI:

Digital Library

[165]

Fadi Wedyan, Dalal Alrmuny, and James M. Bieman. 2009. The effectiveness of automated static analysis tools for fault detection and refactoring prediction. In Proceedings of the International Conference on Software Testing Verification and Validation. 141–150. DOI:

Digital Library

[166]

Jianfeng Wen, Shixian Li, Zhiyong Lin, Yong Hu, and Changqin Huang. 2012. Systematic literature review of machine learning based software development effort estimation models. Info. Softw. Technol. 54 (Jan.2012), 41–59. DOI:

Digital Library

[167]

Claes Wohlin. 2014. Guidelines for snowballing in systematic literature studies and a replication in software engineering. In Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering (EASE’14). ACM, New York, NY, Article 38, 10 pages. DOI:

Digital Library

[168]

Claes Wohlin, Emilia Mendes, Katia Romero Felizardo, and Marcos Kalinowski. 2020. Guidelines for the search strategy to update systematic literature reviews in software engineering. Info. Softw. Technol. 127 (Nov.2020), 106366. DOI:

[169]

C. Wohlin and Rafael Prikladnicki. 2013. Systematic literature reviews in software engineering. Info. Softw. Technol. 55 (2013), 919–920.

Digital Library

[170]

Claes Wohlin, Per Runeson, Martin Hst, Magnus C. Ohlsson, Bjrn Regnell, and Anders Wessln. 2012. Experimentation in Software Engineering. Springer.

[171]

Hong Wu, Lin Shi, Celia Chen, Qing Wang, and Barry Boehm. 2016. Maintenance effort estimation for open source software: A systematic literature review. In Proceedings of the IEEE International Conference on Software Maintenance and Evolution (ICSME’16). IEEE. DOI:

[172]

Yazhou Xie, Majid Ebad Sichani, Jamie E. Padgett, and Reginald DesRoches. 2020. The promise of implementing machine learning in earthquake engineering: A state-of-the-art review. Earthquake Spectra 36, 4 (June2020), 1769–1801. DOI:

[173]

Yanming Yang, Xin Xia, David Lo, Tingting Bi, John Grundy, and Xiaohu Yang. 2022. Predictive models in software engineering: Challenges and opportunities. ACM Trans. Softw. Eng. Methodol. 31, 3, Article 56 (Apr.2022), 72 pages. DOI:

Digital Library

[174]

Yanming Yang, Xin Xia, David Lo, and John Grundy. 2021. A survey on deep learning for software engineering. Comput. Surveys (Dec.2021). DOI:

[175]

Huishi Yin. 2015. A study plan: Open innovation based on internet data mining in software engineering. In Proceedings of the International Conference on Software and System Process. ACM. DOI:

Digital Library

[176]

Maryam Zahid, Zahid Mehmmod, and Irum Inayat. 2017. Evolution in software architecture recovery techniques—A survey. In Proceedings of the 13th International Conference on Emerging Technologies (ICET’17). IEEE, 1–6. DOI:

[177]

Kareshna Zamani, Didar Zowghi, and Chetan Arora. 2021. Machine learning in requirements engineering: A mapping study. In Proceedings of the 29th International Requirements Engineering Conference Workshops (REW’21). IEEE. DOI:

[178]

Samer Zein, Norsaremah Salleh, and John Grundy. 2016. A systematic mapping study of mobile application testing techniques. J. Syst. Softw. 117, C (July2016), 334–356. DOI:

Digital Library

[179]

Du Zhang and Jeffrey J. P. Tsai. 2003. Machine learning and software engineering. Softw. Qual. J. 11 (June2003), 87–119. DOI:

[180]

J. Zheng, L. Williams, N. Nagappan, W. Snipes, J. P. Hudepohl, and M. A. Vouk. 2006. On the value of static analysis for fault detection in software. IEEE Trans. Softw. Eng. 32, 4 (2006), 240–253. DOI:

[181]

You Zhou, He Zhang, Xin Huang, Song Yang, Muhammad Ali Babar, and Hao Tang. 2015. Quality assessment of systematic reviews in software engineering: A tertiary study. In Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering (EASE’15). ACM, New York, NY, Article 14, 14 pages. DOI:

Digital Library

Cited By

Santos IFelizardo KSteinmacher IGerosa M(2025)Software solutions for newcomers’ onboarding in software projects: A systematic literature reviewInformation and Software Technology10.1016/j.infsof.2024.107568177(107568)Online publication date: Jan-2025
https://doi.org/10.1016/j.infsof.2024.107568
Holder DPercy SYavari A(2024)A Review of Port Decarbonisation Options: Identified Opportunities for Deploying Hydrogen TechnologiesSustainability10.3390/su1608329916:8(3299)Online publication date: 15-Apr-2024
https://doi.org/10.3390/su16083299
Drosos GSotiropoulos TSpinellis DMitropoulos D(2024)Bloat beneath Python’s Scales: A Fine-Grained Inter-Project Dependency AnalysisProceedings of the ACM on Software Engineering10.1145/36608211:FSE(2584-2607)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660821
Show More Cited By

Index Terms

Machine Learning for Software Engineering: A Tertiary Study

Recommendations

Machine Learning and Software Engineering
ICTAI '02: Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence

Machine learning deals with the issue of how to build programs that improve their performance at some task through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, ...
A software engineering perspective on engineering machine learning systems: State of the art and challenges
Abstract Context:
Advancements in machine learning (ML) lead to a shift from the traditional view of software development, where algorithms are hard-coded by humans, to ML systems materialized through learning from data. Therefore, ...
Highlights
- A systematic review on the state-of-the-art of engineering Machine Learning systems.
Automating Software Engineering with Machine Learning
ISEC '22: Proceedings of the 15th Innovations in Software Engineering Conference

Software plays a crucial role in our everyday lives. The scarcity of skilled software engineers has become a bottleneck in delivering better software at scale. Can we automate software engineering to help improve developer productivity and software ...

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 12

December 2023

825 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3582891

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 March 2023

Online AM: 30 November 2022

Accepted: 22 November 2022

Revised: 10 November 2022

Received: 22 November 2021

Published in CSUR Volume 55, Issue 12

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Survey

Funding Sources

European Union’s Horizon 2020 research and innovation programme

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
1,815
Total Downloads

Downloads (Last 12 months)606
Downloads (Last 6 weeks)61

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Santos IFelizardo KSteinmacher IGerosa M(2025)Software solutions for newcomers’ onboarding in software projects: A systematic literature reviewInformation and Software Technology10.1016/j.infsof.2024.107568177(107568)Online publication date: Jan-2025
https://doi.org/10.1016/j.infsof.2024.107568
Holder DPercy SYavari A(2024)A Review of Port Decarbonisation Options: Identified Opportunities for Deploying Hydrogen TechnologiesSustainability10.3390/su1608329916:8(3299)Online publication date: 15-Apr-2024
https://doi.org/10.3390/su16083299
Drosos GSotiropoulos TSpinellis DMitropoulos D(2024)Bloat beneath Python’s Scales: A Fine-Grained Inter-Project Dependency AnalysisProceedings of the ACM on Software Engineering10.1145/36608211:FSE(2584-2607)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660821
Zou YWang HLv HZhao STian H(2024)A Cross-Project Defect Prediction Approach Based on Code Semantics and Cross-Version Structural InformationInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402450016534:07(1135-1171)Online publication date: 27-May-2024
https://doi.org/10.1142/S0218194024500165
Doornbos JBennin KBabur ÖValente J(2024)Drone Technologies: A Tertiary Systematic Literature Review on a Decade of ImprovementsIEEE Access10.1109/ACCESS.2024.336467612(23220-23239)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3364676
Wohlin CRuneson PHöst MOhlsson MRegnell BWesslén AWohlin CRuneson PHöst MOhlsson MRegnell BWesslén A(2024)Systematic Literature StudiesExperimentation in Software Engineering10.1007/978-3-662-69306-3_4(51-63)Online publication date: 8-May-2024
https://doi.org/10.1007/978-3-662-69306-3_4
Qayum AZhang MColreavy SChochlov MBuckley JLin DSai A(2024)A Framework and Taxonomy for Characterizing the Applicability of Software Architecture Recovery Approaches: A Tertiary‐Mapping StudySoftware: Practice and Experience10.1002/spe.3364Online publication date: 17-Jul-2024
https://doi.org/10.1002/spe.3364
Bahrehvar MMoshirpour M(2023)Agile Teaching: Automated Student Support and Feedback Generation2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343212(1-9)Online publication date: 18-Oct-2023
https://doi.org/10.1109/FIE58773.2023.10343212

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents