Enhancing User Trust and Interpretability in AI-Driven Feature Request Detection for Mobile App Reviews: An Explainable Approach

Mobile app developers struggle to prioritize updates by identifying feature requests within user reviews. While machine learning models can assist, their complexity often hinders transparency and trust. This paper presents an explainable Artificial Intelligence (AI) approach that combines advanced e...

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Vydané v:	IEEE access Ročník 12; s. 114023 - 114045
Hlavní autori:	Gambo, Ishaya, Massenon, Rhodes, Lin, Chia-Chen, Ogundokun, Roseline Oluwaseun, Agarwal, Saurabh, Pak, Wooguil
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Analytical models Annotations Applications programs Artificial intelligence Artificial neural networks Explainable AI Explainable artificial intelligence Feature extraction feature request detection Interactive systems Machine learning machine learning interpretability mobile app development Mobile applications Mobile computing Reviews Sentiment analysis Software engineering software requirements Support vector machines Trust management Trustworthiness user trust
ISSN:	2169-3536, 2169-3536
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Abstract	Mobile app developers struggle to prioritize updates by identifying feature requests within user reviews. While machine learning models can assist, their complexity often hinders transparency and trust. This paper presents an explainable Artificial Intelligence (AI) approach that combines advanced explanation techniques with engaging visualizations to address this issue. Our system integrates a bidirectional Long Short-Term Memory (BiLSTM) model with attention mechanisms, enhanced by Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). We evaluate this approach on a diverse dataset of 150,000 app reviews, achieving an F1 score of 0.82 and 89% accuracy, significantly outperforming baseline Support Vector Machine (F1: 0.66) and Convolutional Neural Network (CNN) (F1: 0.72) models. Our empirical user studies with developers demonstrate that our explainable approach improves trust (27%) when explanations are provided and correct interpretation (73%). The system's interactive visualizations allowed developers to validate predictions, with over 80% overlap between model-highlighted phrases and human annotations for feature requests. These findings highlight the importance of integrating explainable AI into real-world software engineering workflows. The paper's results and future directions provide a promising approach for feature request detection in app reviews to create more transparent, trustworthy, and effective AI systems.
AbstractList	Mobile app developers struggle to prioritize updates by identifying feature requests within user reviews. While machine learning models can assist, their complexity often hinders transparency and trust. This paper presents an explainable Artificial Intelligence (AI) approach that combines advanced explanation techniques with engaging visualizations to address this issue. Our system integrates a bidirectional Long Short-Term Memory (BiLSTM) model with attention mechanisms, enhanced by Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). We evaluate this approach on a diverse dataset of 150,000 app reviews, achieving an F1 score of 0.82 and 89% accuracy, significantly outperforming baseline Support Vector Machine (F1: 0.66) and Convolutional Neural Network (CNN) (F1: 0.72) models. Our empirical user studies with developers demonstrate that our explainable approach improves trust (27%) when explanations are provided and correct interpretation (73%). The system’s interactive visualizations allowed developers to validate predictions, with over 80% overlap between model-highlighted phrases and human annotations for feature requests. These findings highlight the importance of integrating explainable AI into real-world software engineering workflows. The paper’s results and future directions provide a promising approach for feature request detection in app reviews to create more transparent, trustworthy, and effective AI systems.
Author	Lin, Chia-Chen Massenon, Rhodes Ogundokun, Roseline Oluwaseun Pak, Wooguil Agarwal, Saurabh Gambo, Ishaya
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References	ref57 ref59 Suleman (ref77) ref58 ref53 ref52 ref55 ref51 ref50 ref46 ref45 ref48 ref42 ref41 ref44 ref43 Ribera (ref23) ref49 ref8 ref7 Pang (ref82) 2002 ref9 ref4 Ceci (ref22) 2024 ref3 ref6 ref5 ref100 ref101 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref39 ref38 Liao (ref24) 2021 ref26 ref25 ref20 ref21 Garreau (ref15) ref28 ref27 ref29 ref13 ref12 ref97 Vu (ref66) ref96 ref11 ref99 ref10 ref98 ref17 ref16 ref19 ref18 Samek (ref54) 2017 ref93 ref92 ref95 ref94 ref91 ref90 ref89 Hoon (ref63) 2013 ref86 ref85 ref88 ref87 Zhang (ref47) 2019 ref81 ref84 ref83 ref80 ref79 ref78 ref75 ref104 ref74 ref105 ref102 ref76 ref103 ref2 ref1 ref71 Goyal (ref14) ref70 ref73 ref72 ref68 ref67 Wang (ref56) ref69 ref64 ref65 ref60 ref62 ref61
References_xml	– ident: ref100 doi: 10.1145/3495013 – ident: ref69 doi: 10.1109/SEmotion.2017.6 – ident: ref42 doi: 10.1145/2166966.2166996 – start-page: 2376 volume-title: Proc. 36th Int. Conf. Mach. Learn. ident: ref14 article-title: Counterfactual visual explanations – ident: ref12 doi: 10.1145/3531146.3533168 – ident: ref99 doi: 10.1145/3561048 – ident: ref32 doi: 10.1016/j.ins.2019.02.064 – ident: ref101 doi: 10.1016/j.compbiomed.2018.03.016 – ident: ref10 doi: 10.1109/JIOT.2022.3181607 – ident: ref103 doi: 10.1109/ICCIKE47802.2019.9004385 – ident: ref38 doi: 10.1016/j.inffus.2019.12.012 – ident: ref39 doi: 10.1007/978-3-031-12807-3_3 – ident: ref45 doi: 10.1109/ICCV.2017.74 – ident: ref64 doi: 10.1145/2414536.2414577 – ident: ref98 doi: 10.1145/3236009 – start-page: 57 volume-title: Proc. Int. Conf. Data Sci. ident: ref77 article-title: Google play store app ranking prediction using machine learning algorithm – ident: ref79 doi: 10.1007/s10270-020-00790-w – ident: ref78 doi: 10.1007/978-3-030-49435-3_25 – ident: ref3 doi: 10.1016/j.cmpb.2022.107161 – ident: ref29 doi: 10.1109/ESEM.2015.7321214 – ident: ref105 doi: 10.1145/3411764.3445315 – year: 2019 ident: ref47 article-title: ‘Why should you trust my explanation?’ Understanding uncertainty in LIME explanations publication-title: arXiv:1904.12991 – ident: ref93 doi: 10.1007/s11277-023-10339-x – ident: ref36 doi: 10.1109/RE.2019.00046 – ident: ref27 doi: 10.1145/3409585 – ident: ref52 doi: 10.7717/peerj-cs.340 – ident: ref95 doi: 10.1109/ACCESS.2023.3255990 – ident: ref20 doi: 10.1145/3580305.3599869 – ident: ref59 doi: 10.1109/SANER60148.2024.00030 – ident: ref9 doi: 10.1109/TKDE.2021.3054782 – ident: ref48 doi: 10.1007/978-3-031-14463-9_2 – ident: ref25 doi: 10.1109/RE.2014.6912257 – ident: ref7 doi: 10.1177/03611981221076121 – ident: ref37 doi: 10.1007/s00766-020-00333-1 – ident: ref71 doi: 10.1145/3084226.3084246 – ident: ref16 doi: 10.1109/ICPR.2018.8546302 – ident: ref68 doi: 10.1109/ICSM.2015.7332474 – start-page: 2427 volume-title: Proc. IEEE/ACM 44th Int. Conf. Softw. Eng. (ICSE) ident: ref56 article-title: Where is your app frustrating users? – ident: ref19 doi: 10.14257/ijseia.2018.12.1.03 – ident: ref44 doi: 10.2139/ssrn.3064761 – ident: ref94 doi: 10.1061/(asce)gm.1943-5622.0002058 – ident: ref55 doi: 10.1145/3180155.3180218 – ident: ref11 doi: 10.1016/j.cmpb.2021.106584 – ident: ref50 doi: 10.1016/j.apenergy.2023.122151 – year: 2013 ident: ref63 article-title: An analysis of the mobile app review landscape: Trends and implications – ident: ref67 doi: 10.1109/ICSE.2013.6606749 – ident: ref17 doi: 10.1109/LRA.2021.3070828 – ident: ref89 doi: 10.1016/j.neucom.2020.01.006 – ident: ref91 doi: 10.1007/978-981-16-3637-0_7 – ident: ref6 doi: 10.1201/9781003324140-1 – ident: ref81 doi: 10.1109/ACCESS.2023.3246388 – ident: ref5 doi: 10.1109/TII.2022.3146552 – ident: ref53 doi: 10.1109/ESEM56168.2023.10304855 – year: 2021 ident: ref24 article-title: Question-driven design process for explainable AI user experiences publication-title: arXiv:2104.03483 – ident: ref34 doi: 10.1109/RE51729.2021.00025 – start-page: 1 volume-title: Proc. CEUR Workshop ident: ref23 article-title: Can we do better explanations? A proposal of user-centered explainable AI – ident: ref43 doi: 10.1007/s11257-011-9117-5 – year: 2017 ident: ref54 article-title: Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models publication-title: arXiv:1708.08296 – ident: ref62 doi: 10.5220/0010526901580169 – ident: ref35 doi: 10.1109/REW53955.2021.00031 – ident: ref102 doi: 10.1016/j.eswa.2016.03.028 – ident: ref92 doi: 10.18653/v1/p17-1012 – ident: ref74 doi: 10.1145/3236024.3264595 – ident: ref30 doi: 10.31577/cai_2023_6_1491 – ident: ref73 doi: 10.1109/SANER.2017.7884612 – ident: ref60 doi: 10.1080/10494820.2023.2212708 – ident: ref51 doi: 10.1109/BIBM.2018.8621359 – ident: ref8 doi: 10.1609/aaai.v35i8.16819 – ident: ref76 doi: 10.1109/RE.2018.00018 – ident: ref2 doi: 10.1007/978-3-030-76409-8_2 – ident: ref57 doi: 10.1007/s10515-021-00301-1 – ident: ref84 doi: 10.1109/ACCESS.2017.2779939 – ident: ref96 doi: 10.1007/s11042-022-12410-4 – ident: ref26 doi: 10.1109/ICoICT.2019.8835255 – ident: ref85 doi: 10.3390/make5040068 – ident: ref41 doi: 10.1145/3173574.3174156 – ident: ref13 doi: 10.1145/2939672.2939778 – ident: ref21 doi: 10.1109/JSAC.2023.3322766 – ident: ref80 doi: 10.1007/s10664-022-10222-6 – ident: ref28 doi: 10.1109/RE.2013.6636712 – ident: ref97 doi: 10.1109/ACCESS.2019.2957510 – ident: ref46 doi: 10.1109/REW53955.2021.00033 – ident: ref65 doi: 10.1145/2568225.2568263 – ident: ref86 doi: 10.1016/j.neucom.2019.01.078 – ident: ref1 doi: 10.1145/3531146.3534639 – ident: ref72 doi: 10.1109/RE.2017.71 – volume-title: Annual Number of Mobile App Downloads Worldwide 2023 year: 2024 ident: ref22 – ident: ref88 doi: 10.3390/app10248924 – ident: ref90 doi: 10.1016/j.eswa.2023.119730 – year: 2002 ident: ref82 article-title: Thumbs up? Sentiment classification using machine learning techniques publication-title: arXiv:cs/0205070 – ident: ref40 doi: 10.1016/j.artint.2018.07.007 – ident: ref4 doi: 10.1007/s10916-021-01736-5 – ident: ref58 doi: 10.7717/peerj-cs.874 – start-page: 726 volume-title: Proc. 31st IEEE/ACM Int. Conf. Automated Softw. Eng. (ASE) ident: ref66 article-title: Phrase-based extraction of user opinions in mobile app reviews – start-page: 3620 volume-title: Proc. Int. Conf. Mach. Learn. ident: ref15 article-title: What does lime really see in images? – ident: ref33 doi: 10.1109/REW57809.2023.00024 – ident: ref49 doi: 10.1145/3583780.3614871 – ident: ref18 doi: 10.1145/3507921 – ident: ref75 doi: 10.1109/RE.2017.86 – ident: ref83 doi: 10.4018/IJISMD.2021040101 – ident: ref104 doi: 10.1007/s00766-016-0251-9 – ident: ref87 doi: 10.3390/app10175841 – ident: ref31 doi: 10.31577/cai_2023_6_1445 – ident: ref61 doi: 10.1007/978-3-030-96648-5_9 – ident: ref70 doi: 10.1109/ICSE.2017.18
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SubjectTerms	Analytical models Annotations Applications programs Artificial intelligence Artificial neural networks Explainable AI Explainable artificial intelligence Feature extraction feature request detection Interactive systems Machine learning machine learning interpretability mobile app development Mobile applications Mobile computing Reviews Sentiment analysis Software engineering software requirements Support vector machines Trust management Trustworthiness user trust
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Title	Enhancing User Trust and Interpretability in AI-Driven Feature Request Detection for Mobile App Reviews: An Explainable Approach
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