Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDroidLeaks: Benchmarking Resource Leak Bugs for Android Applications
88
0
0.0
(
0
)
Added by
Yepang Liu
Publication date
2016
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Resource leak bugs in Android apps are pervasive and can cause serious performance degradation and system crashes. In recent years, several resource leak detection techniques have been proposed to assist Android developers in correctly managing system resources. Yet, there exist no common bug benchmarks for effectively and reliably comparing such techniques and quantitatively evaluating their strengths and weaknesses. This paper describes our initial contribution towards constructing such a benchmark. To locate real resource leak bugs, we mined 124,215 code revisions of 34 large-scale open-source Android apps. We successfully found 298 fixed resource leaks, which cover a diverse set of resource classes, from 32 out of the 34 apps. To understand the characteristics of these bugs, we conducted an empirical study, which revealed the root causes of frequent resource leaks in Android apps and common patterns of faults made by developers. With our findings, we further implemented a static checker to detect a common pattern of resource leaks in Android apps. Experiments showed that the checker can effectively locate real resource leaks in popular Android apps, confirming the usefulness of our work.
rate research
Read More
Resource leaks -- a program does not release resources it previously acquired -- are a common kind of bug in Android applications. Even with the help of existing techniques to automatically detect leaks, writing a leak-free program remains tricky. One of the reasons is Androids event-driven programming model, which complicates the understanding of an applications overall control flow. In this paper, we present PlumbDroid: a technique to automatically detect and fix resource leaks in Android applications. PlumbDroid uses static analysis to find execution traces that may leak a resource. The information built for detection also undergirds automatically building a fix -- consisting of release operations performed at appropriate locations -- that removes the leak and does not otherwise affect the applications usage of the resource. An empirical evaluation on resource leaks from the DroidLeaks curated collection demonstrates that PlumbDroids approach is scalable, precise, and produces correct fixes for a variety of resource leak bugs: PlumbDroid automatically found and repaired 50 leaks that affect 9 widely used resources of the Android system, including all those collected by DroidLeaks for those resources; on average, it took just 2 minutes to detect and repair a leak. PlumbDroid also compares favorably to Relda2/RelFix -- the only other fully automated approach to repair Android resource leaks -- since it usually detects more leaks with higher precision and producing smaller fixes. These results indicate that PlumbDroid can provide valuable support to enhance the quality of Android applications in practice.
Since the emergence of Ethereum, blockchain-based decentralized applications (DApps) have become increasingly popular and important. To balance the security, performance, and costs, a DApp typically consists of two layers: an on-chain layer to execute transactions and store crucial data on the blockchain and an off-chain layer to interact with users. A DApp needs to synchronize its off-chain layer with the on-chain layer proactively. Otherwise, the inconsistent data in the off-chain layer could mislead users and cause undesirable consequences, e.g., loss of transaction fees. However, transactions sent to the blockchain are not guaranteed to be executed and could even be reversed after execution due to chain reorganization. Such non-determinism in the transaction execution is unique to blockchain. DApp developers may fail to perform the on-chain-off-chain synchronization accurately due to their lack of familiarity with the complex transaction lifecycle. In this work, we investigate the challenges of synchronizing on-chain and off-chain data in Ethereum-based DApps. We present two types of bugs that could result in inconsistencies between the on-chain and off-chain layers. To help detect such on-chain-off-chain synchronization bugs, we introduce a state transition model to guide the testing of DApps and propose two effective oracles to facilitate the automatic identification of bugs. We build the first testing framework, DArcher, to detect on-chain-off-chain synchronization bugs in DApps. We have evaluated DArcher on 11 popular real-world DApps. DArcher achieves high precision (99.3%), recall (87.6%), and accuracy (89.4%) in bug detection and significantly outperforms the baseline methods. It has found 15 real bugs in the 11 DApps. So far, six of the 15 bugs have been confirmed by the developers, and three have been fixed. These promising results demonstrate the usefulness of DArcher.
Realistic benchmarks of reproducible bugs and fixes are vital to good experimental evaluation of debugging and testing approaches. However, there is no suitable benchmark suite that can systematically evaluate the debugging and testing methods of quantum programs until now. This paper proposes Bugs4Q, a benchmark of thirty-six real, manually validated Qiskit bugs from four popular Qiskit elements (Terra, Aer, Ignis, and Aqua), supplemented with the test cases for reproducing buggy behaviors. Bugs4Q also provides interfaces for accessing the buggy and fix
Due to the convenience of access-on-demand to information and business solutions, mobile apps have become an important asset in the digital world. In the context of the Covid-19 pandemic, app developers have joined the response effort in various ways by releasing apps that target different user bases (e.g., all citizens or journalists), offer different services (e.g., location tracking or diagnostic-aid), provide generic or specialized information, etc. While many apps have raised some concerns by spreading misinformation or even malware, the literature does not yet provide a clear landscape of the different apps that were developed. In this study, we focus on the Android ecosystem and investigate Covid-related Android apps. In a best-effort scenario, we attempt to systematically identify all relevant apps and study their characteristics with the objective to provide a First taxonomy of Covid-related apps, broadening the relevance beyond the implementation of contact tracing. Overall, our study yields a number of empirical insights that contribute to enlarge the knowledge on Covid-related apps: (1) Developer communities contributed rapidly to the Covid-19, with dedicated apps released as early as January 2020; (2) Covid-related apps deliver digital tools to users (e.g., health diaries), serve to broadcast information to users (e.g., spread statistics), and collect data from users (e.g., for tracing); (3) Covid-related apps are less complex than standard apps; (4) they generally do not seem to leak sensitive data; (5) in the majority of cases, Covid-related apps are released by entities with past experience on the market, mostly official government entities or public health organizations.
Static bug finders have been widely-adopted by developers to find bugs in real world software projects. They leverage predefined heuristic static analysis rules to scan source code or binary code of a software project, and report violations to these rules as warnings to be verified. However, the advantages of static bug finders are overshadowed by such issues as uncovered obvious bugs, false positives, etc. To improve these tools, many techniques have been proposed to filter out false positives reported or design new static analysis rules. Nevertheless, the under-performance of bug finders can also be caused by the incorrectness of current rules contained in the static bug finders, which is not explored yet. In this work, we propose a differential testing approach to detect bugs in the rules of four widely-used static bug finders, i.e., SonarQube, PMD, SpotBugs, and ErrorProne, and conduct a qualitative study about the bugs found. To retrieve paired rules across static bug finders for differential testing, we design a heuristic-based rule mapping method which combines the similarity in rules description and the overlap in warning information reported by the tools. The experiment on 2,728 open source projects reveals 46 bugs in the static bug finders, among which 24 are fixed or confirmed and the left are awaiting confirmation. We also summarize 13 bug patterns in the static analysis rules based on their context and root causes, which can serve as the checklist for designing and implementing other rules and or in other tools. This study indicates that the commonly-used static bug finders are not as reliable as they might have been envisaged. It not only demonstrates the effectiveness of our approach, but also highlights the need to continue improving the reliability of the static bug finders.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
