Different Types Of Bugs Research Articles

WolfFuzz: A Dynamic, Adaptive, and Directed Greybox Fuzzer

As the directed greybox fuzzing (DGF) technique advances, it is being extensively utilized in various fields such as defect reproduction, patch testing, and vulnerability identification. Nevertheless, current DGFs waste a significant amount of resources due to their simplistic distance definitions and overly straightforward energy distribution for the seeds. To address these issues, a dynamic distance-weighting-based distance estimation strategy is proposed first, which facilitates strategies for seed distribution that take energy into consideration. Second, to overcome the limitations of current seed energy distribution strategies, the gray wolf optimizer (GWO) is improved by integrating four strategies, leading to the development of the improved gray wolf optimizer (IGWO). Lastly, an adaptive search algorithm is proposed, and the WolfFuzz prototype tool is implemented. In vulnerability recurrence scenarios, WolfFuzz is 3.2× faster on average compared with the baseline and reproduces 76.4% of existing bugs faster. WolfFuzz also discovers nine different types of bugs in seven real-world programs.

Mobile App Crowdsourced Test Report Consistency Detection via Deep Image-and-Text Fusion Understanding

Crowdsourced testing, as a distinct testing paradigm, has attracted much attention in software testing, especially in mobile application (app) testing field. Compared with in-house testing, crowdsourced testing shows superiority with the diverse testing environments when faced with the mobile testing fragmentation problem. However, crowdsourced testing also encounters the low-quality test report problem caused by unprofessional crowdworkers involved with different expertise. In order to handle the submitted reports of uneven quality, app developers have to distinguish high-quality reports from low-quality ones to help the bug inspection. One kind of typical low-quality test report is inconsistent test reports, which means the textual descriptions are not focusing on the attached bug-occurring screenshots. According to our empirical survey, only 18.07% crowdsourced test reports are consistent. Inconsistent reports cause waste on mobile app testing. To solve the inconsistency problem, we propose ReCoDe to detect the consistency of crowdsourced test reports via deep image-and-text fusion understanding. ReCoDe is a two-stage approach that first classifies the reports based on textual descriptions into different categories according to the bug feature. In the second stage, ReCoDe has a deep understanding of the GUI image features of the app screenshots and then applies different strategies to handle different types of bugs to detect the consistency of the crowdsourced test reports. We conduct an experiment on a dataset with over 22k test reports to evaluate ReCoDe, and the results show the effectiveness of ReCoDe in detecting the consistency of crowdsourced test reports. Besides, a user study is conducted to prove the practical value of ReCoDe in effectively helping app developers improve the efficiency of reviewing the crowdsourced test reports.

Software Vulnerabilities and Bug Bounty Programs

Many software developers employ bug bounty programs that award a prize for the detection of bugs in their software. We analyze, in a model with asymmetric information, under which conditions a bug bounty program is beneficial for a software developer. In our model, a bug bounty program allows developers to perfectly discriminate between different types of bugs, and help to avoid reputation costs of exploited bugs. We find that the benefits of bounty program do not only depend on the characteristics of the underlying software but also that a bounty program crucially interacts with other elements of the security strategy.

Usage Patterns and Implementation of Random Forest Methods for Software Risk and Bugs Predictions

The software bugs predictions whereby the datasets of different types of bugs are evaluated for further predictions. In this research manuscript, the pragmatic evaluation of random forest approach is done and compared with results with traditional artificial neural networks (ANN) so that the results can be compared. From the outcome, the extracts from random forest are better on the accuracy level with the test datasets used in a specific format. The process of Random Forest (RF) Approach is adopted in this work that gives the effectual outcomes in most of the cases as compared to ANN and thereby the usage patterns of RF are performance aware. The paradigm of RF is used widely for the engineering optimization to solve the complex problems and generation of the dynamic trees. The outcomes and results obtained and presented in this work is giving the variations in favor random forest based optimization for the software risk management and predictive mining. The need of the proposed work and background of the study includes the effective and performance based software bugs detection. The current problem addressed includes the accuracy and multi-dimensional evaluations. The key methodology adopted here to solve the existing problem is the integration of Random Forest approach and the findings are quite effective and cavernous in assorted aspects

Machine Learning Based Prediction of Complex Bugs in Source Code

During software development and maintenance phases, the fixing of severe bugs are mostly very challenging and needs more efforts to fix them on a priority basis. Several research works have been performed using software metrics and predict fault-prone software module. In this paper, we propose an approach to categorize different types of bugs according to their severity and priority basis and then use them to label software metrics’ data. Finally, we used labeled data to train the supervised machine learning models for the prediction of fault prone software modules. Moreover, to build an effective prediction model, we used genetic algorithm to search those sets of metrics which are highly correlated with severe bugs.

Model Checking-based Software-FMEA: Assessment of Fault Tolerance and Error Detection Mechanisms

Failure Mode and Effects Analysis (FMEA) is a systematic technique to explore the possible failure modes of individual components or subsystems and determine their potential effects at the system level. Applications of FMEA are common in case of hardware and communication failures, but analyzing software failures (SW-FMEA) poses a number of challenges. Failures may originate in permanent software faults commonly called bugs, and their effects can be very subtle and hard to predict, due to the complex nature of programs. Therefore, a behavior-based automatic method to analyze the potential effects of different types of bugs is desirable. Such a method could be used to automatically build an FMEA report about the fault effects, or to evaluate different failure mitigation and detection techniques. This paper follows the latter direction, demonstrating the use of a model checking-based automated SW-FMEA approach to evaluate error detection and fault tolerance mechanisms, demonstrated on a case study inspired by safety-critical embedded operating systems.

Testing the Clinical Programming Software of Cochlear Implant System

Computer based bio-electronic systems are used for replacement of damaged human parts such as Bionic-ear for deafness, Bionic-eye for blindness, Deep Brain Stimulator for diseases of the brain, and Bionic-arm for arm prostheses. Algorithms for controlling bionic system are based on the specific bionic devices like bionic ear with sound processing software and bionic eye with image processing software. A lot of research is going on to improve the performance of bionic systems in respect of achieving near 100% functionality, low cost and smaller sizes with higher reliability and safety. The aim of the paper is to design, development and testing for different bugs in clinical programming software (CPS) of a Cochlear Implant system that is also called as Bionic Ear. The focus of this paper is identification of different types of bugs like some of the common programming errors for CPS with suitable examples, Requirements, and Coding bugs. Testing must show that hazards have been eliminated or controlled to an acceptable level of risk. This collection is to create programmer aware of these errors of CPS of cochlear implant, so they avoid them and also develop the VB.NET based Clinical Programming for data monitoring of patient with profoundly deaf. In this some inadequate programming practices that are often the cause of many kinds of bugs in CPS