Automatically Matching Bug Reports With Related App Reviews

Haering, Marlo; Stanik, Christoph; Maalej, Walid

doi:10.1109/icse43902.2021.00092

Cited by 41 publications

(30 citation statements)

References 45 publications

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“…The general purpose LMs were 'bert-baseuncased', 'roberta-base', 'xlnet-base-cased' and 'distilbertbase-uncased', while the classical IR models were VSM, LDA and LSI. In addition, we also evaluated TraceNN [22] as discussed in Section 8, and DeepMatcher [64].…”

Section: Rq1: How Well Does Nltrace Perform Without the Benefit Of Do...mentioning

confidence: 99%

“…Our study further investigates the effectiveness of applying LM-based approaches to address the text-to-text tracing task instead of text-tocode. Other LM based trace models, such as DeepMatcher [64], used DistillBert as an alternative encoder of VSM and RNN; however the output of DistillBert was directly applied to Cosine Similarity without fine-tuning. We argue that this method is relatively weak because it only uses the knowledge from the pretraining stage without the benefit of calibration with the target project through fine-tuning.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing Automated Software Traceability by Transfer Learning from Open-World Data

Lin¹,

Poudel²,

Yu³

et al. 2022

Preprint

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Software requirements traceability is a critical component of the software engineering process, enabling activities such as requirements validation, compliance verification, and safety assurance. However, the cost and effort of manually creating a complete set of trace links across natural language artifacts such as requirements, design, and test-cases can be prohibitively expensive. Researchers have therefore proposed automated link-generation solutions primarily based on information-retrieval (IR) techniques; however, these solutions have failed to deliver the accuracy needed for full adoption in industrial projects. Improvements can be achieved using deep-learning traceability models; however, their efficacy is impeded by the limited size and availability of project-level artifacts and links to serve as training data. In this paper, we address this problem by proposing and evaluating several deep-learning approaches for text-to-text traceability. Our method, named NLTrace, explores three transfer learning strategies that use datasets mined from open world platforms. Through pretraining Language Models (LMs) and leveraging adjacent tracing tasks, we demonstrate that NLTrace can significantly improve the performance of LM based trace models when training links are available. In such scenarios NLTrace outperforms the best performing classical IR method with an 188% improvement in F2 score and 94.01% in Mean Average Precision (MAP). It also outperforms the general LM based trace model by 7% and 23% for F2 and MAP respectively. In addition, NLTrace can adapt to low-resource tracing scenarios where other LM models can not. The knowledge learned from adjacent tasks enables NLTrace to outperform VSM models by 28% F2 on generation challenges when presented with a small number of training examples.

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Section: Rq1: How Well Does Nltrace Perform Without the Benefit Of Do...mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Enhancing Automated Software Traceability by Transfer Learning from Open-World Data

Lin¹,

Poudel²,

Yu³

et al. 2022

Preprint

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“…Wu et al [41] created a Chinese dataset from the Chinese Apple App Store and built a regression model to identify key features of app by analyzing app description and positive/negative user reviews. Haering et al [50] focused on the gap between technically-written bug reports with colloquially-written app reviews, extracting issues from app reviews and matching them to bug reports. Henao et al [51] proposed a framework for mining feature requests and bug reports from tweets and app store reviews via transfer learning.…”

Section: B App Review Miningmentioning

confidence: 99%

Listening to Users' Voice: Automatic Summarization of Helpful App Reviews

Gao¹,

Li²,

Qi³

et al. 2022

Preprint

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App reviews are crowdsourcing knowledge of user experience with the apps, providing valuable information for app release planning, such as major bugs to fix and important features to add. There exist prior explorations on app review mining for release planning, however, most of the studies strongly rely on pre-defined classes or manually-annotated reviews. Also, the new review characteristic, i.e., the number of users who rated the review as helpful, which can help capture important reviews, has not been considered previously.In the paper, we propose a novel framework, named SOLAR, aiming at accurately summarizing helpful user reviews to developers. The framework mainly contains three modules: The review helpfulness prediction module, topic-sentiment modeling module, and multi-factor ranking module. The review helpfulness prediction module assesses the helpfulness of reviews, i.e., whether the review is useful for developers. The topic-sentiment modeling module groups the topics of the helpful reviews and also predicts the associated sentiment, and the multi-factor ranking module aims at prioritizing semantically representative reviews for each topic as the review summary. Experiments on five popular apps indicate that SOLAR is effective for review summarization and promising for facilitating app release planning.

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“…Other researchers tried to match user reviews with bug reports written by developers. For example, Häring et al [6] proposed an automatic approach DeepMatcher to match problem reports in app reviews to bug reports in issue trackers by using deep learning algorithms. Palomba et al proposed CRISTAL [7] to trace informative crowd reviews into code changes, in order to monitor the extent to which developers accommodate crowd requests and follow-up user reactions as reflected in their ratings.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Although informative user reviews were reported to have valuable information (e.g., feature requests and bug reports) for developers, there is still no clarity on whether user reviews are actually taken into account by developers and implemented in the new release. Some researchers traced informative user reviews to source code changes to support evolution of successful apps [6] [7]. However, these authors focused their exploration on open source apps, rather than all the mobile apps.…”

Section: Introductionmentioning

confidence: 99%

The Role of User Reviews in App Updates: A Preliminary Investigation on App Release Notes

Wang

Liu

Peng

et al. 2021

2021 28th Asia-Pacific Software Engineering Conference (APSEC)

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Release planning for mobile apps has recently become an area of active research. Prior research in this area concentrated on the analysis of release notes and on tracking user reviews to support app evolution with issue trackers. However, little is known about the impact of user reviews on the evolution of mobile apps. Our work explores the role of user reviews in app updates based on release notes. For this purpose, we collected user reviews and release notes of Spotify, the 'number one' app in the 'Music' category in Apple App Store, as the research data. Then, we manually removed non-informative parts of each release note, and manually determined the relevance of the app reviews with respect to the release notes. We did this by using Word2Vec calculation techniques based on the top 80 app release notes with the highest similarities. Our empirical results show that more than 60% of the matched reviews are actually irrelevant to the corresponding release notes. When zooming in at these relevant user reviews, we found that around half of them were posted before the new release and referred to requests, suggestions, and complaints. Whereas, the other half of the relevant user reviews were posted after updating the apps and concentrated more on bug reports and praise.

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Automatically Matching Bug Reports With Related App Reviews

Cited by 41 publications

References 45 publications

Enhancing Automated Software Traceability by Transfer Learning from Open-World Data

Enhancing Automated Software Traceability by Transfer Learning from Open-World Data

Listening to Users' Voice: Automatic Summarization of Helpful App Reviews

The Role of User Reviews in App Updates: A Preliminary Investigation on App Release Notes

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