Code Smell Detection Using Whale Optimization Algorithm

Software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their capabilities.Code smells arise as one of the obstacles in the software industry.They are characteristics of software source code that indicate a deeper problem in design.These smells appear not only in the design but also in software implementation.Code smells introduce bugs,affect software maintainability,and lead to higher maintenance costs.Uncovering code smells can be formulated as an optimization problem of finding the best detection rules.Although researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be improved.Previous research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their types.Our research improves code smell detection by applying a search-based technique;we use the Whale Optimization Algorithm as a classifier to find ideal detection rules.Applying this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass variance.The proposed framework adopts if-then detection rules during the software development life cycle.Those rules identify the types for both medium and large projects.Experiments are conducted on five open-source software projects to discover nine smell types that mostly appear in codes.The proposed detection framework has an average of 94.24%precision and 93.4%recall.These accurate values are better than other search-based algorithms of the same field.The proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and cost.Additionally,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.

DM Code Key Point Detection Algorithm Based on CenterNet

Data Matrix(DM)codes have been widely used in industrial production.The reading of DM code usually includes positioning and decoding.Accurate positioning is a prerequisite for successful decoding.Traditional image processing methods have poor adaptability to pollution and complex backgrounds.Although deep learning-based methods can automatically extract features,the bounding boxes cannot entirely fit the contour of the code.Further image processing methods are required for precise positioning,which will reduce efficiency.Because of the above problems,a CenterNet-based DM code key point detection network is proposed,which can directly obtain the four key points of the DM code.Compared with the existing methods,the degree of fitness is higher,which is conducive to direct decoding.To further improve the positioning accuracy,an enhanced loss function is designed,including DM code key point heatmap loss,standard DM code projection loss,and polygon Intersection-over-Union(IoU)loss,which is beneficial for the network to learn the spatial geometric characteristics of DM code.The experiment is carried out on the self-made DM code key point detection dataset,including pollution,complex background,small objects,etc.,which uses the Average Precision(AP)of the common object detection metric as the evaluation metric.AP reaches 95.80%,and Frames Per Second(FPS)gets 88.12 on the test set of the proposed dataset,which can achieve real-time performance in practical applications.

A New Framework for Software Vulnerability Detection Based on an Advanced Computing

The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest today.This paper proposes a new framework called DrCSE to improve software vulnerability detection.It uses an intelligent computation technique based on the combination of two methods:Rebalancing data and representation learning to analyze and evaluate the code property graph(CPG)of the source code for detecting abnormal behavior of software vulnerabilities.To do that,DrCSE performs a combination of 3 main processing techniques:(i)building the source code feature profiles,(ii)rebalancing data,and(iii)contrastive learning.In which,the method(i)extracts the source code’s features based on the vertices and edges of the CPG.The method of rebalancing data has the function of supporting the training process by balancing the experimental dataset.Finally,contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers away.The experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabilities using the Verum dataset.As a result,the method proposed in the article has brought a pretty good performance in all metrics,especially the Precision and Recall scores of 39.35%and 69.07%,respectively,proving the efficiency of the DrCSE Framework.It performs better than other approaches,with a 5%boost in Precision and a 5%boost in Recall.Overall,this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date.

Iterative Noncoherent Block Detection of Coded MPSK for Cooperative Relay Systems

Maximum likelihood(ML) noncoherent block detection techniques are investigated for block-coded MPSK modulation in cooperative decode-and-forward relay systems over slow fading channels.A decision-directed iterative Viterbi algorithm(IVA) is derived for a suboptimal ML noncoherent detection.Simulation results show that the IVA can approach the error performances of the exhaustive detection method but at a lower complexity.

Dual-channel Method for Fast Long PN-code Acquisition

Long PN-code acquisition is a difficult and time-consuming task due to long code period.To accelerate acquisition,folding methods like XFAST are widely used.In highdynamic environment however,the application of those methods are largely restricted due to nonnegligible residual frequency.This paper proposes a new dual-channel method for fast acquisition of long PN-code.In the proposed method,both non-overlapping local PNcode blocks are employed to correlate with input sample block;the detection process is eased through finding the maximum value among correlation results and verification is made with all the full and partial peaks taken into account.False alarm probabilities from analysis of the verification process are derived.Both theoretical and Monte Carlo simulations reveal that,with respect to acquisition probability and mean acquisition time under the same false alarm rate,dual-channel method has advantage over zero-padding and XFAST based folding methods under certain false alarm probabilities.

Improved iterative convergence method in Q-ary LDPC coded high order PR-CPM

The Q-ary low-density parity-check（LDPC） coded high order partial response continuous phase modulation（PR-CPM） with double iterative loops is investigated. This scheme shows significant improvements in power and bandwidth efficiency, but at the expense of long iterative decoding delay and computational complexity induced by the improper match between the demodulator and the decoder. To address this issue, the convergence behavior of Q-ary LDPC coded CPM is investigated for the Q=2 and Q〉2 cases, and an optimized design method based on the extrinsic information transfer chart is proposed to improve the systematic iterative efficiency. Simulation results demonstrate that the proposed method can achieve a perfect tradeoff between iterative decoding delay and bit error rate performance to satisfy real-time applications.

Detection of semantically similar code

The traditional similar code detection approaches are limited in detecting semantically similar codes, impeding their applications in practice. In this paper, we have improved the traditional metrics-based approach as well as the graph- based approach and presented a metrics-based and graph- based combined approach. First, source codes are represented as augmented system dependence graphs. Then, metrics- based candidate similar code extraction is performed to filter out most of the dissimilar code pairs so as to lower the computational complexity. After that, code normalization is performed on the candidate similar codes to remove code variations so as to detect similar code at the semantic level. Finally, program matching is performed on the normalized control dependence trees to output semantically similar codes. Experiment results show that our approach can detect similar codes with code variations, and it can be applied to large software.

Proof of Activity Protocol for IoMT Data Security

The Internet of Medical Things(IoMT)is an online device that senses and transmits medical data from users to physicians within a time interval.In,recent years,IoMT has rapidly grown in the medicalfield to provide healthcare services without physical appearance.With the use of sensors,IoMT applications are used in healthcare management.In such applications,one of the most important factors is data security,given that its transmission over the network may cause obtrusion.For data security in IoMT systems,blockchain is used due to its numerous blocks for secure data storage.In this study,Blockchain-assisted secure data management framework(BSDMF)and Proof of Activity(PoA)protocol using malicious code detection algorithm is used in the proposed data security for the healthcare system.The main aim is to enhance the data security over the networks.The PoA protocol enhances high security of data from the literature review.By replacing the malicious node from the block,the PoA can provide high security for medical data in the blockchain.Comparison with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio,precision ratio,security,and efficiency and less response time for Blockchain-enabled healthcare systems.

Detection of long bones based on Barker code excited ultrasonic guided waves

Single pulse excited ultrasonic guided wave surfers high attenuation during the propagation in long bones.This results in small amplitude and low signal-to-noise ratio（SNR）of measured signals.Thus,the Barker code excitation is introduced into long bone detection to improve the quality of received signals,due to its efficiency in increasing amplitude and SNR.Both simulation and in vitro experiment were performed,and the results were decoded by the weighted match filter（WMF） and the finite impulse response- least squares inverse filter（FIRLSIF）,respectively.The comparison between the results of Barker code excitation and sine pulse excitation was presented.For 13-bit Barker code excitation,WMF produced 13 times larger amplitude than sine pulse excitation,while FIR-LSIF achieved higher peak-sidelobe-level（PSL） of -63.59 dB and better performance in noise suppression.The results show that the Barker code excited guided waves have the potential to be applied to the long bone detection.

Verification of SEU resistance in 65 nm high-performance SRAM with dual DICE interleaving and EDAC mitigation strategies

A dual double interlocked storage cell(DICE)interleaving layout static random-access memory(SRAM)is designed and manufactured based on 65 nm bulk complementary metal oxide semiconductor technology.The single event upset(SEU)cross sections of this memory are obtained via heavy ion irradiation with a linear energy transfer(LET)value ranging from 1.7 to 83.4 MeV/(mg/cm^(2)).Experimental results show that the upset threshold(LETth)of a 4 KB block is approximately 6 MeV/(mg/cm^(2)),which is much better than that of a standard unhardened SRAM with an identical technology node.A 1 KB block has a higher LETth of 25 MeV/(mg/cm^(2))owing to the use of the error detection and correction(EDAC)code.For a Ta ion irradiation test with the highest LET value(83.4 MeV/(mg/cm^(2))),the benefit of the EDAC code is reduced significantly because the multi-bit upset proportion in the SEU is increased remarkably.Compared with normal incident ions,the memory exhibits a higher SEU sensitivity in the tilt angle irradiation test.Moreover,the SEU cross section indicates a significant dependence on the data pattern.When comprehensively considering HSPICE simulation results and the sensitive area distributions of the DICE cell,it is shown that the data pattern dependence is primarily associated with the arrangement of sensitive transistor pairs in the layout.Finally,some suggestions are provided to further improve the radiation resistance of the memory.By implementing a particular design at the layout level,the SEU tolerance of the memory is improved significantly at a low area cost.Therefore,the designed 65 nm SRAM is suitable for electronic systems operating in serious radiation environments.

Anti-obfuscation Binary Code Clone Detection Based on Software Gene

Information technology facilitates people’s lives greatly,while it also brings many security issues, such as code plagiarism, softwarein-fringement, and malicious code. In order to solve the problems,reverse engineering is applied to analyze abundant binary code manually,which costs a lot of time. However, due to the maturity of differentobfuscation techniques, the disassembly code generated from the samefunction differs greatly in the opcode and control flow graph throughdifferent obfuscation options. This paper propose a method inspired bynatural language processing, to realize the semantic similarity matchingof binary code in basic block granularity and function granularity. In thesimilarity matching task of binary code obtained by different obfuscationoptions of LLVM, the indicator reaches 99%, which is better than theexisting technologies.

Predicting Code Smells and Analysis of Predictions: Using Machine Learning Techniques and Software Metrics

Code smell detection is essential to improve software quality, enhancing software maintainability, and decrease the risk of faults and failures in the software system. In this paper, we proposed a code smell prediction approach based on machine learning techniques and software metrics. The local interpretable model-agnostic explanations (LIME) algorithm was further used to explain the machine learning model's predictions and interpretability. The datasets obtained from Fontana et al. were reformed and used to build binary-label and multi-label datasets. The results of 10-fold cross-validation show that the performance of tree-based algorithms (mainly Random Forest) is higher compared with kernel-based and network-based algorithms. The genetic algorithm based feature selection methods enhance the accuracy of these machine learning algorithms by selecting the most relevant features in each dataset. Moreover, the parameter optimization techniques based on the grid search algorithm significantly enhance the accuracy of all these algorithms. Finally, machine learning techniques have high potential in predicting the code smells, which contribute to detect these smells and enhance the software's quality.

Coarse-to-fine visual autonomous unmanned aerial vehicle landing on a moving platform

Autonomous unmanned aerial vehicle(UAV)landing is a challenging task,especially on a moving platform in an unstructured environment.Under such a scenario,successful UAV landing is mainly affected by poor UAV localization performance.To solve this problem,we propose a coarse-to-fine visual autonomous UAV landing system based on an enhanced visual positioning approach.The landing platform is marked with a specially designed QR code marker,which is developed to improve the landing accuracy when the UAV approaches the landing site.Besides,we employ the you only look once framework to enhance the visual positioning accuracy,thereby promoting the landing platform detection when the UAV is flying far away.The framework recognizes the QR code and decodes the position of a UAV by the corner points of the QR code.Further,we use the Kalman filter to fuse the position data decoded from the QR code with those from the inertia measurement unit sensor.Then,the position data are used for UAV landing with a developed hierarchical landing strategy.To verify the effectiveness of the proposed system,we performed experiments in different environments under various light conditions.The experimental results demonstrate that the proposed system can achieve UAV landing with high accuracy,strong adaptability,and robustness.In addition,it can achieve accurate landing in different operating environments without external real-time kinematic global positioning system(RTK-GPS)signals,and the average landing error is 11.5 cm,which is similar to the landing error when using RTK-GPS signals as the ground truth.