arXiv subjects

Yiling Lou

Publications and source records attributed to Yiling Lou.

39 records · Page 3Linked to original sources

arXiv2021

An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications

Deep Learning (DL) is finding its way into a growing number of mobile software applications. These software applications, named as DL based mobile applications (abbreviated as mobile DL apps) integrate DL models trained using large-scale data with DL programs. A DL program encodes the structure of a desirable DL model and the process by which the model is trained using training data. Due to the increasing dependency of current mobile apps on DL, software engineering (SE) for mobile DL apps has become important. However, existing efforts in SE research community mainly focus on the development of DL models and extensively analyze faults in DL programs. In contrast, faults related to the deployment of DL models on mobile devices (named as deployment faults of mobile DL apps) have not been well studied. Since mobile DL apps have been used by billions of end users daily for various purposes including for safety-critical scenarios, characterizing their deployment faults is of enormous importance. To fill the knowledge gap, this paper presents the first comprehensive study on the deployment faults of mobile DL apps. We identify 304 real deployment faults from Stack Overflow and GitHub, two commonly used data sources for studying software faults. Based on the identified faults, we construct a fine-granularity taxonomy consisting of 23 categories regarding to fault symptoms and distill common fix strategies for different fault types. Furthermore, we suggest actionable implications and research avenues that could further facilitate the deployment of DL models on mobile devices.

cs.SE↗

arXiv2020

Interactive Patch Filtering as Debugging Aid

It is widely recognized that program repair tools need to have a high precision to be useful, i.e., the generated patches need to have a high probability to be correct. However, it is fundamentally difficult to ensure the correctness of the patches, and many tools compromise other aspects of repair performance such as recall for an acceptable precision. In this paper we ask a question: can a repair tool with a low precision be still useful? To explore this question, we propose an interactive filtering approach to patch review, which filters out incorrect patches by asking questions to the developers. Our intuition is that incorrect patches can still help understand the bug. With proper tool support, the benefit outweighs the cost even if there are many incorrect patches. We implemented the approach as an Eclipse plugin tool, InPaFer, and evaluated it with a simulated experiment and a user study with 30 developers. The results show that our approach improve the repair performance of developers, with 62.5% more successfully repaired bugs and 25.3% less debugging time in average. In particular, even if the generated patches are all incorrect, the performance of the developers would not be significantly reduced, and could be improved when some patches provide useful information for repairing, such as the faulty location and a partial fix.

cs.SE↗

arXiv2019

Can Automated Program Repair Refine Fault Localization?

Software bugs are prevalent in modern software systems and notoriously hard to debug manually. Therefore, a large body of research efforts have been dedicated to automated software debugging, including both automated fault localization and program repair. However, the existing fault localization techniques are usually ineffective on real-world software systems while even the most advanced program repair techniques can only fix a small ratio of real-world bugs. Although fault localization and program repair are inherently connected, we observe that in the literature their only connection is that program repair techniques usually use off-the-shelf fault localization techniques (e.g., Ochiai) to determine the potential candidate statements/elements for patching. In this work, we explore their connection in the other direction, i.e., can program repair in turn help with fault localization? In this way,we not only open a new dimension for more powerful fault localization, but also extend the application scope of program repair to all possible bugs (not only the bugs that can be directly automatically fixed).We have designed ProFL, a simplistic approach using patch-execution results (from program repair) as the feedback information for fault localization. The experimental results on the widely used Defects4J benchmark show that the basic ProFL can already localize 161 of the 395 studied bugs within Top-1, while state-of-the-art spectrum and mutation based fault localization techniques at most localize 117 within Top-1. We also demonstrate ProFL's effectiveness under different settings. Lastly, we show that ProFL can further boost state-of-the-art fault localization via both unsupervised and supervised learning.

cs.SE↗