{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T10:34:21Z","timestamp":1756895661863},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"26","license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s00521-023-08756-x","type":"journal-article","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T02:01:52Z","timestamp":1688004112000},"page":"19523-19539","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A deep intelligent framework for software risk prediction using improved firefly optimization"],"prefix":"10.1007","volume":"35","author":[{"given":"Suresh Kumar","family":"Pemmada","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Janmenjoy","family":"Nayak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bighnaraj","family":"Naik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,29]]},"reference":[{"issue":"06","key":"8756_CR1","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1142\/S0219622016500401","volume":"15","author":"J Li","year":"2016","unstructured":"Li J, Li M, Wu D, Dai Q, Song H (2016) A bayesian networks-based risk identification approach for software process risk: the context of chinese trustworthy software. Int J Inf Technol Decis Mak 15(06):1391\u20131412. https:\/\/doi.org\/10.1142\/S0219622016500401","journal-title":"Int J Inf Technol Decis Mak"},{"key":"8756_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s11334-020-00379-y","author":"P Suresh Kumar","year":"2021","unstructured":"Suresh Kumar P, Behera HS, Nayak J, Naik B (2021) A pragmatic ensemble learning approach for effective software effort estimation. Innov Syst Softw Eng. https:\/\/doi.org\/10.1007\/s11334-020-00379-y","journal-title":"Innov Syst Softw Eng"},{"key":"8756_CR3","unstructured":"The Economic impacts of inadequate infrastructure of software testing. RTI. https:\/\/www.nist.gov\/system\/files\/documents\/director\/planning\/report02-3.pdf"},{"key":"8756_CR4","doi-asserted-by":"publisher","unstructured":"Rosen C, Grawi B, Shihab E (2015) Commit guru: analytics and risk prediction of software commits. In: Proceedings of the 2015 10th joint meeting on foundations of software engineering\u2013ESEC\/FSE 2015, p 966\u2013969. https:\/\/doi.org\/10.1145\/2786805.2803183","DOI":"10.1145\/2786805.2803183"},{"issue":"1","key":"8756_CR5","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/S0950-5849(01)00217-8","volume":"44","author":"J Drew Procaccino","year":"2002","unstructured":"Drew Procaccino J, Verner JM, Overmyer SP, Darter ME (2002) Case study: factors for early prediction of software development success. Inf Softw Technol 44(1):53\u201362. https:\/\/doi.org\/10.1016\/S0950-5849(01)00217-8","journal-title":"Inf Softw Technol"},{"issue":"1","key":"8756_CR6","first-page":"55","volume":"4","author":"TT Moores","year":"1996","unstructured":"Moores TT, Champion REM (1996) A methodology for measuring the risk associated with a software. Australas J Inf Syst 4(1):55\u201363","journal-title":"Australas J Inf Syst"},{"issue":"3","key":"8756_CR7","doi-asserted-by":"publisher","first-page":"18","DOI":"10.5120\/cae-1527","volume":"1","author":"S Patil","year":"2015","unstructured":"Patil S, Ade R (2015) Generic approach for goal driven software requirement risk management. Commun Appl Electron 1(3):18\u201321. https:\/\/doi.org\/10.5120\/cae-1527","journal-title":"Commun Appl Electron"},{"key":"8756_CR8","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1016\/j.neucom.2014.07.068","volume":"150","author":"B Krawczyk","year":"2015","unstructured":"Krawczyk B (2015) One-class classifier ensemble pruning and weighting with firefly algorithm. Neurocomputing 150:490\u2013500. https:\/\/doi.org\/10.1016\/j.neucom.2014.07.068","journal-title":"Neurocomputing"},{"issue":"4","key":"8756_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3340544","volume":"28","author":"M Tufano","year":"2019","unstructured":"Tufano M, Watson C, Bavota G, Di Penta M, White M, Poshyvanyk D (2019) An empirical study on learning bug-fixing patches in the wild via neural machine translation. ACM Trans Softw Eng Methodol 28(4):1\u201329. https:\/\/doi.org\/10.1145\/3340544","journal-title":"ACM Trans Softw Eng Methodol"},{"key":"8756_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s12530-022-09423-7","author":"SK Pemmada","year":"2022","unstructured":"Pemmada SK, Behera HS, Nayak J, Naik B (2022) Correlation-based modified long short-term memory network approach for software defect prediction. Evol Syst. https:\/\/doi.org\/10.1007\/s12530-022-09423-7","journal-title":"Evol Syst"},{"key":"8756_CR11","doi-asserted-by":"publisher","unstructured":"Wang S, Liu T, Tan L (2016) Automatically learning semantic features for defect prediction. In: Proceedings of the 38th international conference on software engineering, p 297\u2013308. https:\/\/doi.org\/10.1145\/2884781.2884804","DOI":"10.1145\/2884781.2884804"},{"key":"8756_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11334-021-00399-2","volume":"17","author":"P Suresh Kumar","year":"2021","unstructured":"Suresh Kumar P, Behera HS, Nayak J, Naik B (2021) Bootstrap aggregation ensemble learning-based reliable approach for software defect prediction by using characterized code feature. Innov Syst Softw Eng 17:1\u201322. https:\/\/doi.org\/10.1007\/s11334-021-00399-2","journal-title":"Innov Syst Softw Eng"},{"issue":"2","key":"8756_CR13","doi-asserted-by":"publisher","first-page":"137","DOI":"10.25103\/jestr.152.17","volume":"15","author":"PS Kumar","year":"2022","unstructured":"Kumar PS, Nayak J, Behera HS (2022) Model-based software defect prediction from software quality characterized code features by using stacking ensemble learning. J Eng Sci Technol Rev 15(2):137\u2013155. https:\/\/doi.org\/10.25103\/jestr.152.17","journal-title":"J Eng Sci Technol Rev"},{"key":"8756_CR14","doi-asserted-by":"publisher","unstructured":"Gu X, Zhang H, Kim S (2018) Deep code search. In: Proceeding international conference on software engineering, p 933\u2013944. https:\/\/doi.org\/10.1145\/3180155.3180167","DOI":"10.1145\/3180155.3180167"},{"key":"8756_CR15","doi-asserted-by":"publisher","first-page":"100288","DOI":"10.1016\/j.cosrev.2020.100288","volume":"38","author":"P Suresh Kumar","year":"2020","unstructured":"Suresh Kumar P, Behera HS, Kumari AK, Nayak J, Naik B (2020) Advancement from neural networks to deep learning in software effort estimation: perspective of two decades. Comput Sci Rev 38:100288. https:\/\/doi.org\/10.1016\/j.cosrev.2020.100288","journal-title":"Comput Sci Rev"},{"key":"8756_CR16","doi-asserted-by":"publisher","first-page":"60309","DOI":"10.1109\/ACCESS.2021.3072380","volume":"9","author":"MS Khan","year":"2021","unstructured":"Khan MS, Jabeen F, Ghouzali S, Rehman Z, Naz S, Abdul W (2021) Metaheuristic algorithms in optimizing deep neural network model for software effort estimation. IEEE Access 9:60309\u201360327. https:\/\/doi.org\/10.1109\/ACCESS.2021.3072380","journal-title":"IEEE Access"},{"key":"8756_CR17","doi-asserted-by":"publisher","unstructured":"Zhang J, Zou F, Zhu J (2018) Android malware detection based on deep learning. In: 2018 IEEE 4th international conference on computer and communications (ICCC), p 2190\u20132194. https:\/\/doi.org\/10.1109\/CompComm.2018.8781037","DOI":"10.1109\/CompComm.2018.8781037"},{"key":"8756_CR18","volume-title":"Deep learning in software engineering","author":"AW Cody","year":"2020","unstructured":"Cody AW (2020) Deep learning in software engineering. The College of William and Mary, Williamsburg"},{"key":"8756_CR19","volume-title":"Nature-inspired optimization algorithms","author":"XS Yang","year":"2014","unstructured":"Yang XS (2014) Nature-inspired optimization algorithms. Elsevier Inc., Amsterdam"},{"key":"8756_CR20","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/B978-0-12-809633-8.20507-4","volume-title":"Encyclopedia of bioinformatics and computational biology","author":"D Simoncini","year":"2019","unstructured":"Simoncini D, Zhang KYJ (2019) Population-based sampling and fragment-based de novo protein structure prediction. Encyclopedia of bioinformatics and computational biology. Elsevier, Amsterdam, pp 774\u2013784"},{"key":"8756_CR21","doi-asserted-by":"publisher","unstructured":"Kennedy J, Eberhart R (2021) Particle swarm optimization. In: Proceedings of ICNN\u201995\u2013international conference on neural networks, p 1942\u20131948. https:\/\/doi.org\/10.1109\/ICNN.1995.488968","DOI":"10.1109\/ICNN.1995.488968"},{"key":"8756_CR22","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1007\/0-306-48056-5_9","volume-title":"Handbook of metaheuristics","author":"M Dorigo","year":"2003","unstructured":"Dorigo M, St\u00fctzle T (2003) The ant colony optimization metaheuristic: algorithms, applications, and advances. Handbook of metaheuristics. Springer, Cham, pp 250\u2013285"},{"key":"8756_CR23","doi-asserted-by":"publisher","unstructured":"Zhao S-Z, Suganthan PN, Das S (2010) Dynamic multi-swarm particle swarm optimizer with sub-regional harmony search. In: IEEE congress on evolutionary computation, p 1\u20138. https:\/\/doi.org\/10.1109\/CEC.2010.5586323","DOI":"10.1109\/CEC.2010.5586323"},{"issue":"1","key":"8756_CR24","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s00521-013-1367-1","volume":"24","author":"X-S Yang","year":"2014","unstructured":"Yang X-S, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Appl 24(1):169\u2013174. https:\/\/doi.org\/10.1007\/s00521-013-1367-1","journal-title":"Neural Comput Appl"},{"key":"8756_CR25","doi-asserted-by":"crossref","unstructured":"Yang X-S (2009) Firefly algorithms for multimodal optimization. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), LNCS, p 169\u2013178","DOI":"10.1007\/978-3-642-04944-6_14"},{"issue":"12","key":"8756_CR26","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1080\/00207160.2014.907405","volume":"91","author":"S Yu","year":"2014","unstructured":"Yu S, Su S, Lu Q, Huang L (2014) A novel wise step strategy for firefly algorithm. Int J Comput Math 91(12):2507\u20132513. https:\/\/doi.org\/10.1080\/00207160.2014.907405","journal-title":"Int J Comput Math"},{"key":"8756_CR27","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.amc.2015.04.065","volume":"263","author":"S Yu","year":"2015","unstructured":"Yu S, Zhu S, Ma Y, Mao D (2015) A variable step size firefly algorithm for numerical optimization. Appl Math Comput 263:214\u2013220. https:\/\/doi.org\/10.1016\/j.amc.2015.04.065","journal-title":"Appl Math Comput"},{"key":"8756_CR28","unstructured":"Fister I, Yang X-S, Fister I, Brest J (2012) Memetic firefly algorithm for combinatorial optimization. In: Proceedings 5th international conference bioinspired optim. Methods their applied. BIOMA 2012, p 75\u201386. [Online]. Available: http:\/\/arxiv.org\/abs\/1204.5165"},{"key":"8756_CR29","doi-asserted-by":"crossref","unstructured":"Shaukat ZS, Naseem R, Zubair M (2018) A dataset for software requirements risk prediction. In: 2018 IEEE international conference on computational science and engineering (CSE). https:\/\/figshare.com\/articles\/Requirement_Risk_Data_arff\/5878819. Accessed 11 Dec 11 2020","DOI":"10.1109\/CSE.2018.00022"},{"issue":"3","key":"8756_CR30","doi-asserted-by":"publisher","first-page":"101","DOI":"10.30630\/joiv.1.3.35","volume":"1","author":"HAM Salih","year":"2017","unstructured":"Salih HAM, Ammar HH (2017) Model-based resource utilization and performance risk prediction using machine learning techniques. JOIV Int J Inform Vis 1(3):101. https:\/\/doi.org\/10.30630\/joiv.1.3.35","journal-title":"JOIV Int J Inform Vis"},{"key":"8756_CR31","first-page":"266","volume-title":"Advanced intelligent computing theories and applications with aspects of contemporary intelligent computing techniques","author":"Z Xu","year":"2015","unstructured":"Xu Z, Yang B, Guo P (2015) Software risk prediction based on the hybrid algorithm of genetic algorithm and decision tree. Advanced intelligent computing theories and applications with aspects of contemporary intelligent computing techniques. Springer, Berlin Heidelberg, pp 266\u2013274"},{"issue":"2","key":"8756_CR32","doi-asserted-by":"publisher","first-page":"440","DOI":"10.4304\/jsw.7.2.440-449","volume":"7","author":"Y Hu","year":"2012","unstructured":"Hu Y, Mo X, Zhang X, Zeng Y, Du J, Xie K (2012) Intelligent analysis model for outsourced software project risk using constraint-based bayesian network. J Softw 7(2):440\u2013449. https:\/\/doi.org\/10.4304\/jsw.7.2.440-449","journal-title":"J Softw"},{"key":"8756_CR33","doi-asserted-by":"publisher","unstructured":"Hu Y, Zhang X, Sun X, Liu M, Du J (2009) An intelligent model for software project risk prediction. In: 2009 International conference on information management, innovation management and industrial engineering, p 629\u2013632. https:\/\/doi.org\/10.1109\/ICIII.2009.157","DOI":"10.1109\/ICIII.2009.157"},{"key":"8756_CR34","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1007\/978-3-540-74282-1_30","volume-title":"Advanced intelligent computing theories and applications. With aspects of contemporary intelligent computing techniques","author":"Z Xu","year":"2007","unstructured":"Xu Z, Yang B, Guo P (2007) Software risk prediction based on the hybrid algorithm of genetic algorithm and decision tree. Advanced intelligent computing theories and applications. With aspects of contemporary intelligent computing techniques. Springer, Berlin, Heidelberg, pp 266\u2013274"},{"key":"8756_CR35","doi-asserted-by":"publisher","unstructured":"Appukkutty K, Ammar HH, Popstajanova KG (2005) Software requirement risk assessment using UML. In: The 3rd ACS\/IEEE international conference oncomputer systems and applications, p 591\u2013594. https:\/\/doi.org\/10.1109\/AICCSA.2005.1387101","DOI":"10.1109\/AICCSA.2005.1387101"},{"issue":"2","key":"8756_CR36","doi-asserted-by":"publisher","first-page":"168","DOI":"10.3390\/electronics10020168","volume":"10","author":"R Naseem","year":"2021","unstructured":"Naseem R et al (2021) Empirical assessment of machine learning techniques for software requirements risk prediction. Electronics 10(2):168. https:\/\/doi.org\/10.3390\/electronics10020168","journal-title":"Electronics"},{"key":"8756_CR37","doi-asserted-by":"publisher","unstructured":"Shaukat ZS, Naseem R, Zubair M (2018) A dataset for software requirements risk prediction. In: 2018 IEEE International conference on computational science and engineering (CSE), p 112\u2013118. https:\/\/doi.org\/10.1109\/CSE.2018.00022","DOI":"10.1109\/CSE.2018.00022"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08756-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08756-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08756-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T15:27:44Z","timestamp":1692026864000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08756-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,29]]},"references-count":37,"journal-issue":{"issue":"26","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["8756"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08756-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,29]]},"assertion":[{"value":"20 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that this manuscript has no conflict of interest with any other published source and has not been published previously (partly or in full). No data have been fabricated or manipulated to support our conclusions.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}