Top

Empirical Software Engineering

Published in:

01-11-2021

An empirical study of IoT topics in IoT developer discussions on Stack Overflow

Authors: Gias Uddin, Fatima Sabir, Yann-Gaël Guéhéneuc, Omar Alam, Foutse Khomh

Published in: Empirical Software Engineering | Issue 6/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Internet of Things (IoT) is defined as the connection between places and physical objects (i.e., things) over the Internet via smart computing devices. It is a rapidly emerging paradigm that encompasses almost every aspect of our modern life, such as smart home, cars, and so on. With interest in IoT growing, we observe that the IoT discussions are becoming prevalent in online developer forums, such as Stack Overflow (SO). An understanding of such discussions can offer insights into the prevalence, popularity, and difficulty of various IoT topics. For this paper, we download a large number of SO posts that contain discussions about various IoT technologies. We apply topic modeling on the textual contents of the posts. We label the topics and categorize the topics into hierarchies. We analyze the popularity and difficulty of the topics. Our study offers several findings. First, IoT developers discuss a range of topics in SO related to Hardware, Software, Network, and Tutorials. Second, secure messaging using IoT devices from the Network category is the most prevalent topic, followed by scheduling of IoT script in the Software category. Third, all the topic categories are evolving rapidly in SO, i.e., new questions are being added more and more in SO about IoT tools and techniques. Fourth, the “How” type of questions are asked more across the three topic categories (Software, Network, and Hardware), although a large number of questions are also of the “What” type: IoT developers are using SO not only to discuss how to address a problem related to IoT, but also to learn what the different IoT techniques and tools offer. Fifth, topics related to data parsing and micro-controller configuration are the most popular. Sixth, topics related to multimedia streaming and Bluetooth are the most difficult. Our study findings have implications for all four different IoT stakeholders: tool builders, developers, educators, and researchers. For example, IoT developers and newcomers can use our findings on topic popularity to learn about popular IoT techniques. Educators and researchers can make more tutorials or develop new techniques to make difficult IoT topics easier. IoT tool builders can look at our identified topics and categories to learn about IoT developers’ preferences, which then can help them develop new tools or enhance their current offerings.

previous article Topic modeling in software engineering research

next article Where were the repair ingredients for Defects4j bugs?

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

https://meta.stackexchange.com/questions/235092

https://meta.stackexchange.com/questions/186910

https://api.stackexchange.com/docs/related-tags

Q_i and A_i denote a question or an answer in SO with ID i

Abdellatif A, Costa D, Badran K, Abdalkareem R, Shihab E (2020) Challenges in chatbot development: A study of stack overflow posts. In: 17th international conference on mining software repositories. ACM, Seoul, New York

Agrawal A, Fu W, Menzies T (2018) What is wrong with topic modeling? and how to fix it using search-based software engineering. Inf Softw Technol 98:74–88CrossRef

Ahmed S, Bagherzadeh M (2018) What do concurrency developers ask about?: A large-scale study using stack overflow. In: Proceedings of the 12th ACM/IEEE international symposium on empirical software engineering and measurement, page Article No. 30

Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M (2015) Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun Surv Tutor 17(4):2347–2376CrossRef

Aly M, Khomh F, Yacout S (2021) What do practitioners discuss about iot and industry 4.0 related technologies? characterization and identification of iot and industry 4.0 categories in stack overflow discussions. Internet Things 14:100364CrossRef

McCallum AK (2019) MALLET: A machine learning for language toolkit. http://mallet.cs.umass.edu/

Andrzejewski D, Mulhern A, Liblit B, Zhu X (2007) Statistical debugging using latent topic models. In: European conference on machine learning. Springer, pp 6–17

Arun R, Suresh V, Madhavan CEV, Murthy MNN (2010) On finding the natural number of topics with latent dirichlet allocation: some observations. In: Proceedings of the 14th Pacific-Asia conference on advances in knowledge discovery and data Mining. pp 391–402

Asaduzzaman M, Mashiyat AS, Roy CK, Schneider KA (2013) Answering questions about unanswered questions of stack overflow. In: Proceedings of the 10th working conference on mining software repositories. pp 87–100

Asuncion HU, Asuncion AU, Tylor RN (2010) Software traceability with topic modeling. In: Proceedings of 32nd international conference on software engineering. pp 95–104

Atzori L, Iera A, Morabito G (2010) The internet of things: A survey. Comput Netw 54(15):2787–2805MATHCrossRef

Bagherzadeh M, Khatchadourian R (2019) Going big: A large-scale study on what big data developers ask. In: Proceedings of the 2019 27th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering, ESEC/FSE 2019. ACM, New York, pp 432–442

Bagherzadeh M, Khatchadourian R (2019) Going big: a large-scale study on what big data developers ask. In: Proceedings of the 2019 27th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering. pp 432–442

Bandeira A, Medeiros CA, Paixao M, Maia PH (2019) We need to talk about microservices: an analysis from the discussions on stackoverflow. In: 2019 IEEE/ACM 16th international conference on mining software repositories (MSR). pp 255–259. IEEE

Barua A, Thomas SW, Hassan AE (2012) What are developers talking about? an analysis of topics and trends in stack overflow. Empir Softw Eng :1–31

Bavota G, Gethers M, Oliveto R, Poshyvanyk D, Lucia AD (2014) Improving software modularization via automated analysis of latent topics and dependencies. ACM Trans Softw Eng Methodol (TOSEM) 23(1):1–33CrossRef

Bavota G, Oliveto R, Gethers M, Poshyvanyk D, Lucia AD (2014) Methodbook: Recommending move method refactorings via relational topic models. IEEE Trans Softw Eng 40(7):671–694CrossRef

Biggers LR, Bocovich C, Capshaw R, Eddy BP, Etzkorn LH, Kraft NA (2014) Configuring latent dirichlet allocation based feature location. J Empir Softw Eng 19(3):465–500CrossRef

Blei DM (2012) Probabilistic topic models. Commun ACM 55 (4):77–84CrossRef

Blei DM, Lafferty JD (2007) A correlated topic model of science. Ann Appl Sci 1(1):17–35MathSciNetMATH

Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(4-5):993–1022MATH

Booth T, Stumpf S, Bird J, Jones S (2016) Crossed wires: Investigating the problems of end-user developers in a physical computing task. In: Proceedings of the 2016 CHI conference on human factors in computing systems. pp 3485–3497

Bridge C (2011) Unstructured data and the 80 percent rule. Tersedia di: http://www.clarabridge.com/default. aspx

Bukhari A, Liu X (2018) A web service search engine for large-scale web service discovery based on the probabilistic topic modeling and clustering. SOCA 12(2):169–182CrossRef

Chae BK (2019) The evolution of the internet of things (iot): A computational text analysis. Telecomm Policy 43(10):101848CrossRef

Chaqfeh MA, Mohamed N (2012) Challenges in middleware solutions for the internet of things. In: International conference on collaboration technologies and systems (CTS). pp 21–26

Chase J (2013) The evolution of internet of things. Technical report, Texas Instruments

Chen T-H, Thomas SW, Nagappan M, Hassan AE (2012) Explaining software defects using topic models. In: 9th working conference on mining software repositories. pp 189–198

Chen T-HP, Thomas SW, Hassan AE (2016) A survey on the use of topic models when mining software repositories. Empir Softw Eng 21(5):1843–1919CrossRef

Cleary B, Exton C, Buckley J, English M (2009) An empirical analysis of information retrieval based concept location techniques in software comprehension. Empir Softw Eng 14:93–130CrossRef

Dagenais B, Ossher H, Bellamy RKE, Robillard MP, de Vries JP (2010) Moving into a new software project landscape. In: 32nd ACM/IEEE international conference on software engineering. pp 275–284

Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R (1990) Indexing by latent semantic analysis. J Amer Soc Inf Sci 41(6):391–407CrossRef

Dit B, Revelle M, Gethers M, Poshyvanyk D (2013) Feature location in source code: a taxonomy and survey. J Softw Evol Process 25(1):53–95CrossRef

Dit B, Revelle M, Poshyvanyk D (2013) Integrating information retrieval, execution and link analysis algorithms to improve feature location in software. Empir Softw Eng 18(2):277–309CrossRef

Fahland D, Lo D, Maoz S (2013) Mining branching-time scenarios. In: Proc. IEEE/ACM international conference on automated software engineering. pp 443–453

Frustaci M, Pace P, Aloi G, Fortino G (2017) Evaluating critical security issues of the iot world: Present and future challenges. IEEE Internet Things J 5(4):2483–2495CrossRef

Girdhar Y, Giguere P, Dudek G (2013) Autonomous adaptive underwater exploration using online topic modeling. In: Experimental robotics. Springer, pp 789–802

Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (iot): A vision, architectural elements, and future directions. Future Gener Comput Syst 29(7):1645–1660CrossRef

Han J, Shihab E, Wan Z, Deng S, Xia X (2020) What do programmers discuss about deep learning frameworks. Empir Softw Eng

Hong L, Davison BD (2010) Empirical study of topic modeling in twitter. In: Proceedings of the first workshop on social media analytics. pp 80–88

Hu J, Sun X, Lo D, Li B (2015) Modeling the evolution of development topics using dynamic topic models. In: IEEE 22nd international conference on software analysis, evolution, and reengineering. pp 3–12

Hudson W (2013) Card sorting. In: Soegaard M, Dam RF (eds) The encyclopedia of human-computer interaction. The interaction design foundation. 2nd edn.

Kamilaris A, Botteghi N (2020) The penetration of internet of things in robotics: Towards a web of robotic things. arXiv:2001.05514

Kang K, Choo J, Kim Y (2020) Whose opinion matters? analyzing relationships between bitcoin prices and user groups in online community. Soc Sci Comput Rev 38(6):686–702CrossRef

Kendall MG (1938) A new measure of rank correlation. Biometrika 30(1):81–93MATHCrossRef

Khan MA, Salah K (2018) Iot security: Review, blockchain solutions, and open challenges. Futur Gener Comput Syst 82:395–411CrossRef

Kruskal WH (1957) Historical notes on the wilcoxon unpaired two-sample test. J Am Stat Assoc 52:356–360MATHCrossRef

Lee S-E, Choi M, Kim S (2017) How and what to study about iot: Research trends and future directions from the perspective of social science. Telecommun Policy 41(10):1056–1067CrossRef

Li H, Chen T-HP, Shang W, Hassan AE (2018) Studying software logging using topic models. Empir Softw Eng 23:2655–2694CrossRef

Liao Y, de Freitas Rocha Loures E, Deschamps F (2018) Industrial internet of things: A systematic literature review and insights. IEEE Internet Things J 5(6):4515–4525CrossRef

Linstead E, Bajracharya S, Ngo T, Rigor P, Lopes C, Baldi P (2009) Sourcerer: Mining and searching internet-scale software repos. Data Min Knowl Disc 18(2):300–326CrossRef

Linton M, Teo EGS, Bommes E, Chen C, Härdle WK (2017) Dynamic topic modelling for cryptocurrency community forums. In: Applied quantitative finance, Springer. pp 355–372

Liu B (2016) Sentiment analysis and subjectivity. In: Indurkhya N, Damerau FJ (eds) Handbook of natural language processing. 2nd edn. CRC Press, Taylor and Francis Group

Liu L, Tang L, Dong W, Yao S, Zhou W (2016) An overview of topic modeling and its current applications in bioinformatics. SpringerPlus 5 (1):1608CrossRef

Liu X, Sun X, Li B, Zhu J (2014) Pfn: A novel program feature network for program comprehension. In: 2014 IEEE/ACIS 13th international conference on computer and information science (ICIS). IEEE, pp 349–354

Marjani M, Nasaruddin F, Gani A, Karim A, Hashem IAT, Siddiqa A, Yaqoob I (2017) Big iot data analytics: Architecture, opportunities, and open research challenges. IEEE Access 5(1):5247–5261

Mathews E, Guclu SS, Liu Q, Ozcelebi T, Lukkien J (2017) The internet of lights: An open reference architecture and implementation for intelligent solid state lighting systems. Energies 10(8):1187CrossRef

McHugh ML (2012) Interrater reliability: the kappa statistic. Biochem Med 22(3):276–282MathSciNetCrossRef

Mens T, Serebrenik A, Cleve A (2014) Evolving software systems, vol 190. Springer, BerlinCrossRef

Mentsiev AU, Mentsiev AU, Amirova EF (2020) Iot and mechanization in agriculture: problems, solutions, and prospects. In: IOP conference series: earth and environmental science, vol 548, p 032035

Minoli D, Sohraby K, Occhiogrosso B (2017) Iot security (IoTSec) mechanisms for e-health and ambient assisted living applications. In: IEEE/ACM international conference on connected health: applications, systems and engineering technologies, pp 13–18

Mocrii D, Chen Y, Musilek P. (2018) Iot-based smart homes: A review of system architecture, software, communications, privacy and security. Internet Things 1:81–98CrossRef

Nabli H, Djemaa RB, Amor IAB (2018) Efficient cloud service discovery approach based on lda topic modeling. J Syst Softw 146:233–248CrossRef

Nguyen TT, Nguyen TN, Phuong TM (2011) Topic-based defect prediction (nier track). In: Proceedings of the 33rd international conference on software engineering. pp 932–935

Nie K, Zhang L (2012) Software feature location based on topic models. In: 2012 19th asia-pacific software engineering conference, vol 1. IEEE, pp 547–552

NLTK (2016) Sentiment analysis. http://www.nltk.org/howto/sentiment.html

Overflow S (2020) Stack Overflow Questions. https://stackoverflow.com/questions/. Last accessed on 14 November 2020

Panichella A, Dit B, Oliveto R, Penta MD, Poshyvanyk D, Lucia AD (2013) How to effectively use topic models for software engineering tasks? an approach based on genetic algorithms. In: International conference on software engineering. pp 522–531

Panichella A, Dit B, Oliveto R, Penta MD, Poshyvanyk D, Lucia AD (2016) Parameterizing and assembling ir-based solutions for se tasks using genetic algorithms. In: 23rd IEEE international conference on software analysis, evolution, and reengineering

Pathak AR, Pandey M, Rautaray S (2019) Adaptive model for dynamic and temporal topic modeling from big data using deep learning architecture. Int J Intell Syst Appl 11(6):13

Ponzanelli L, Bavota G, Di Penta M, Oliveto R, Lanza M (2016) Prompter: Turning the IDE into a self-confident programming assistant. Empir Softw Eng 21(5):2190–2231CrossRef

Ponzanelli L, Mocci A, Bacchelli A, Lanza M (2014) Improving low quality stack overflow post detection. In: Poceedings of the 30th international conference on software maintenance and evolution. pp 541–544

Porter MF (1997) Morgan Kaufmann Publishers Inc. In: Jones KS, Willett PK (eds) Readings in information retrieval. 1st edn.

Poshyvanyk D, Gethers M, Marcus A (2013) Concept location using formal concept analysis and information retrieval. ACM Trans Softw Eng Methodol (TOSEM) 21(4):1–34CrossRef

Poshyvanyk D, Guéhéneuc Y-G, Marcus A, Antoniol G, Rajlich VT (2007) Feature location using probabilistic ranking of methods based on execution scenarios and information retrieval. IEEE Trans Softw Eng 33(6):420–432CrossRef

Pretz K (2013) The next evolution of the internet. IEEE Mag Inst 50(5)

Rahman LF, Ozcelebi T, Lukkien J (2018) Understanding iot systems: a life cycle approach. Procedia Comput Sci 130:1057–1062CrossRef

Rao S, Kak AC (2011) Retrieval from software libraries for bug localization: a comparative study of generic and composite text models. In: 8th working conference on mining software repositories, pp 43–52

Řehůřek R, Sojka P (2010) Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks. pp 45–50

Ren X, Xing Z, Xia X, Li G, Sun J (2019) Discovering, explaining and summarizing controversial discussions in community q&a sites. In: 34th IEEE/ACM international conference on automated software engineering. pp 151–162

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

Rosen C, Shihab E (2015) What are mobile developers asking about? a large scale study using stack overflow. Empir Softw Eng 33

Rosen C, Shihab E (2016) What are mobile developers asking about? a large scale study using stack overflow. J Empir Softw Eng 21(3):1192–1223CrossRef

Rosen C, Shihab E (2016) What are mobile developers asking about? a large scale study using stack overflow. Empir Softw Eng 21(3):1192–1223CrossRef

Salton G, Buckley C (1990) Improving retrieval performance by relevance feedback. J Am Soc Inf Sci 41(4):288–297CrossRef

Savage T, Dit B, Gethers M, Poshyvanyk D (2010) Topic xp: Exploring topics in source code using latent dirichlet allocation. In: 2010 IEEE international conference on software maintenance. IEEE, pp 1–6

Sethi P, Sarangi SR (2017) Internet of things architectures, protocols, and applications. J Electric Comput Eng :2017

Shahid MN (2020) A cross-disciplinary review of blockchain research trends and methodologies: topic modeling approach. In: Proceedings of the 53rd Hawaii international conference on system sciences

Sharma N, Shamkuwar M, Singh I (2019) The history, present and future with iot. Internet Things Big Data Anal Smart Gener 154(1):27–51CrossRef

Singh S, Sharma PK, Yoon B, Shojafar M, Cho GH, Ra I-H (2020) Convergence of blockchain and artificial intelligence in iot network for the sustainable smart city. Sustain Cities Soc 63:102364CrossRef

Stack Exchange Inc. (2019) Stack exchange data dump. https://archive.org/details/stackexchange

Stack Overflow (2010) Statistics: What is the average response time on Stack Overflow? https://meta.stackexchange.com/questions/61301

Stack Overflow (2021) Tags. https://stackoverflow.com/tags

Steyver M, Griffiths T (2007) Probabilistic topic models. In: Handbook of latent semantic analysis, vol 427, pp 424–440

Sun X, Li B, Leung H, Li B, Li Y (2015) Msr4sm: Using topic models to effectively mining software repositories for software maintenance tasks. Inf Softw Technol 66:671–694CrossRef

Sun X, Li B, Li Y, Chen Y (2015) What information in software historical repositories do we need to support software maintenance tasks? an approach based on topic model. Comput Inf Sci :22–37

Sun X, Liu X, Li B, Duan Y, Yang H, Hu J (2016) Exploring topic models in software engineering data analysis A survey. In: 17th IEEE/ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed computing. pp 357–362

Terragni V, Liu Y, Cheung S-C (2016) Csnippex: automated synthesis of compilable code snippets from q&a sites. In: Poceedings of the 25th international symposium on software testing and analysis. pp 118–129

Thomas SW, Adams B, Hassan AE, Blostein D (2011) Modeling the evolution of topics in source code histories. In: 8th working conference on mining software repositories. pp 173–182

Thomas SW, Adams B, Hassan AE, Blostein D (2014) Studying software evolution using topic models. Sci Comput Program 80(B):457–479CrossRef

Tian K, Revelle M, Poshyvanyk D (2009) Using latent dirichlet allocation for automatic categorization of software. In: 6th international working conference on mining software repositories. pp 163–166

Uddin G, Baysal O, Guerrouj L, Khomh F (2018) Understanding how and why developers seek and analyze API-related opinions. IEEE Trans Softw Eng 37. Under review

Uddin G, Khomh F (2017) Automatic summarization of API reviews. In: Proceedings of 32nd IEEE/ACM international conference on automated software engineering. p 12

Uddin G, Robillard MP (2015) How api documentation fails. IEEE Softw 32(4):76–83CrossRef

Vayansky I, Kumar SA (2020) A review of topic modeling methods. Inf Syst 94:101582CrossRef

Verma S, Kawamoto Y, Fadlullah ZM, Nishiyama H, Kato N (2017) A survey on network methodologies for real-time analytics of massive iot data and open research issues. IEEE Commun Surv Tutor 19(3):1457–1477CrossRef

Wan Z, Xia X, Hassan AE (2019) What do programmers discuss about blockchain? a case study on the use of balanced lda and the reference architecture of a domain to capture online discussions about blockchain platforms across stack exchange communities. IEEE Trans Softw Eng 1(1):24

Wan Z, Xia X, Hassan AE (2019) What is discussed about blockchain? a case study on the use of balanced lda and the reference architecture of a domain to capture online discussions about blockchain platforms across the stack exchange communities. IEEE Trans Softw Eng

Wang J, Gao P, Ma Y, He K, Hung PC (2017) A web service discovery approach based on common topic groups extraction. IEEE Access 5:10193–10208CrossRef

Wang S, Wan J, Zhang D, Li D, Zhang C (2016) Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput Netw 101:158–168CrossRef

Weyrich M, Ebert C (2016) Reference architectures for the internet of things. IEEE Softw 33(1):112–116CrossRef

Whitmore A, Agarwal A, Da Xu L (2015) The internet of things—a survey of topics and trends. Inf Syst Front 17(2):261–274CrossRef

Wohlin C, Runeson P, Höst M., Ohlsson MC, Regnell B, Wesslén A (2000) Experimentation in software engineering: an introduction. Kluwer Academic Publishers, NorwellMATHCrossRef

Xie X, Zhang W, Yang Y, Wang Q (2012) Dretom: Developer recommendation based on topic models for bug resolution. In: Proceedings of the 8th international conference on predictive models in software engineering. pp 19–28

Xu B, Xing Z, Xia X, Lo D (2017) Answerbot: automated generation of answer summary to developers’ technical questions. In: Proc. 32nd IEEE/ACM international conference on automated software engineering. pp 706–716

Yang D, Hussain A, Lopes CV (2016) From query to usable code: an analysis of stack overflow code snippets. In: Poceedings of the 13th international conference on mining software repositories. pp 391–402

Yang G, Zhang T, Lee B (2014) Towards semi-automatic bug triage and severity prediction based on topic model and multi-feature of bug reports. In: 2014 IEEE 38th annual computer software and applications conference. IEEE, pp 97–106

Yang X-L, Lo D, Xia X, Wan Z-Y, Sun J-L (2016) What security questions do developers ask? a large-scale study of stack overflow posts. J Comput Sci Technol 31(5):910–924CrossRef

Yang Z, Yue Y, Yang Y, Peng Y, Wang X, Liu W (2011) Study and application on the architecture and key technologies for IoT. In: International conference on multimedia technology. pp 747–751

Zhang T, Upadhyaya G, Reinhardt A, Rajan H, Kim M (2018) Are code examples on an online q&a forum reliable?: a study of api misuse on stack overflow. In: Proceedings of the 40th international conference on software engineering. pp 886–896

Zhang Z-K, Cho MCY, Wang C-W, Hsu C-W, Chen C-K, Shieh S (2014) Iot security: Ongoing challenges and research opportunities. In: IEEE 7th international conference on service-oriented computing and applications. pp 230–234

Zheng Y, Zhang Y-J, Larochelle H (2015) A deep and autoregressive approach for topic modeling of multimodal data. IEEE Trans Pattern Analy Mach Intell 38(6):1056–1069CrossRef

Title: An empirical study of IoT topics in IoT developer discussions on Stack Overflow
Authors: Gias Uddin
Fatima Sabir
Yann-Gaël Guéhéneuc
Omar Alam
Foutse Khomh
Publication date: 01-11-2021
Publisher: Springer US
Published in: Empirical Software Engineering / Issue 6/2021
Print ISSN: 1382-3256
Electronic ISSN: 1573-7616
DOI: https://doi.org/10.1007/s10664-021-10021-5

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 6/2021

The forgotten role of search queries in IR-based bug localization: an empirical study

Single-state state machines in model-driven software engineering: an exploratory study

A pragmatic approach for hyper-parameter tuning in search-based test case generation

How are project-specific forums utilized? A study of participation, content, and sentiment in the Eclipse ecosystem

Characterizing refactoring graphs in Java and JavaScript projects

Why and what happened? Aiding bug comprehension with automated category and causal link identification

Premium Partner