Trace Files Research Articles

A Hybrid Branch Prediction Approach For High-Performance Processors

Background: In a parallel processor, the pipeline cannot fetch the conditional instructions with the next clock cycle, leading to a pipeline stall. Therefore, conditional instructions create a problem in the pipeline because the proper path can only be known after the branch execution. To accurately predict branches, a significant predictor is proposed for the prediction of the conditional branch instruction. Method: In this paper, a single branch prediction and a correlation branch prediction scheme are applied to the different trace files by using the concept of saturating counters. Further, a hybrid branch prediction scheme is proposed, which uses both global and local branch information, providing more accuracy than the single and correlation branch prediction schemes. Results: Firstly, a single branch prediction and correlation branch prediction technique are applied to the trace files using saturating counters. By comparison, it can be observed that a correlation branch prediction technique provides better results by enhancing the accuracy rate of 2.25% than the simple branch prediction. Further, a hybrid branch prediction scheme is proposed, which uses both global and local branch information, providing more accuracy than the single and correlation branch prediction schemes. The results suggest that the proposed hybrid branch prediction schemes provide an increased accuracy rate of 3.68% and 1.43% than single branch prediction and correlation branch prediction. Conclusion: The proposed hybrid branch prediction scheme gives a lower misprediction rate and higher accuracy rate than the simple branch prediction scheme and correlation branch prediction scheme.

Mitochondrial DNA-based species testing of confiscated aquatic wildlife in the Philippines

Illegal wildlife trade (IWT) is a global concern and a major threat to biodiversity conservation. In the Philippines, enforcement of laws related to aquatic wildlife is actively pursued pursuant to the Republic Act (RA) 9147 otherwise known as the Wildlife Resources Conservation and Protection Act and RA 8550 or the Philippine Fisheries Code of 1998 as amended by RA 10654. The National Fisheries Research and Development Institute - Genetic Fingerprinting Laboratory (NFRDI-GFL), functions as lead laboratory for identifying aquatic species in the country using genetic markers pursuant to Philippine Executive Order 154, series of 2013. Philippine law enforcement agencies send confiscated aquatic wildlife samples to NFRDI-GFL for identifying species since 2012 to present. DNA-based species identification of wildlife often uses mitochondrial genes such as Cytochrome c oxidase 1 (CO1) and Cytochrome b (Cytb). Here, a total of 114 individual from confiscation events mostly from Metro Manila and Davao regions were identified using CO1 and Cytb, and their trace files stored in the NFRDI-GFL database. Of the 114 individuals, 26 species have been identified; 16 are under threatened category under IUCN, 17 are listed under the CITES Appendices and 21 are considered as regulated under Philippine Laws. Taken together, this study shows that IWT for aquatic species is evident in the country. Moreover, the use of mitochondrial DNA-based methods can provide accurate species identification, hence allowing for better monitoring and tracking of IWT to support fisheries management and conservation in the country.

Enhancing OLSR Protocol by an Advanced Greedy Forwarding Mechanism for VANET in Smart Cities

The future Intelligent Transport System "ITS" is one of the major challenges of the smart city. It requires fast and efficient communication between vehicles (vehicle-to-vehicle “V2V”), to ensure information exchange in order to improve safety, which reduces accidents and consequently save lives, hence the need of the Vehicular Ad Hoc Network “VANET”, which makes possible the inter-vehicle communication. This network is characterized by a variable topology. Therefore, MANET (Mobile Adhoc NETwork) routing protocols need a few tweaks to be available for the vehicle environment. In this paper, we start by exposing some works related to the evaluation of the most well-known protocols. After a comparative study, we deduce that the OLSR (Optimized Link State Routing) protocol outperforms other routing protocols in terms of End-to-End Delay (EED) and Packet Delivery Ratio (PDR). In addition, we note that the Greedy forwarding “GF” mechanism is suited for the VANET environment, which has been improved and called Greedy forwarding Advanced “GFA”, to overcome the stationary node problem. Our approach improves the OLSR protocol to be more suitable and efficient for VANET by introducing the GFA mechanism. Moreover, we compare our approach to the OLSR classic version. In this work, we use a realistic scenario from Open Street Map (OSM), and simulations are performed using SUMO (Simulation of Urban MObility). The trace files generated from SUMO are used for further simulation in NS-3 (Network Simulator) to validate our proposition. The simulation results are analyzed and discussed. Our approach performs best compared to OLSR in terms of EED and PDR, especially for dense traffic.

SnackNTM: An Open-Source Software for Sanger Sequencing-based Identification of Nontuberculous Mycobacterial Species.

BackgroundSequence-based identification is one of the most effective methods for species-level identification of nontuberculous mycobacteria (NTM). However, it is time-consuming because of the bioinformatics processes involved, including sequence trimming, consensus sequence generation, and public database searches. We developed a simple and fully automated software that enabled species-level identification of NTM from trace files, SnackNTM (https//github.com/Young-gonKim/SnackNTM).MethodsJAVA programing language was used for software development. The SnackNTM diagnostic algorithm utilized 16S rRNA gene sequences, according to the Clinical & Laboratory Standards Institute guidelines, and an rpoB gene region was adjunctively utilized to narrow down the species. The software performance was validated using trace files of 234 clinical cases, comprising 217 consecutive cases and 17 additionally selected cases of unique species.ResultsSnackNTM could analyze multiple cases at once, and all the bioinformatics processes required for sequence-based NTM identification were automatically performed with a single mouse click. SnackNTM successfully identified 95.9% (208/217) of consecutive clinical cases, and the results showed 99.0% (206/208) agreement with manual classification results. SnackNTM successfully identified all 17 cases of unique species. In a processing time comparison test, the analysis and reporting of 30 cases, which took 150 minutes manually, took only 40 minutes with SnackNTM.ConclusionsSnackNTM is expected to reduce the workload for NTM identification, especially in clinical laboratories that process large numbers of cases.

Targeted Sanger sequencing to recover key mutations in SARS-CoV-2 variant genome assemblies produced by next-generation sequencing.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is adaptively evolving to ensure its persistence within human hosts. It is therefore necessary to continuously monitor the emergence and prevalence of novel variants that arise. Importantly, some mutations have been associated with both molecular diagnostic failures and reduced or abrogated next-generation sequencing (NGS) read coverage in some genomic regions. Such impacts are particularly problematic when they occur in genomic regions such as those that encode the spike (S) protein, which are crucial for identifying and tracking the prevalence and dissemination dynamics of concerning viral variants. Targeted Sanger sequencing presents a fast and cost-effective means to accurately extend the coverage of whole-genome sequences. We designed a custom set of primers to amplify a 401 bp segment of the receptor-binding domain (RBD) (between positions 22698 and 23098 relative to the Wuhan-Hu-1 reference). We then designed a Sanger sequencing wet-laboratory protocol. We applied the primer set and wet-laboratory protocol to sequence 222 samples that were missing positions with key mutations K417N, E484K, and N501Y due to poor coverage after NGS sequencing. Finally, we developed SeqPatcher, a Python-based computational tool to analyse the trace files yielded by Sanger sequencing to generate consensus sequences, or take preanalysed consensus sequences in fasta format, and merge them with their corresponding whole-genome assemblies. We successfully sequenced 153 samples of 222 (69 %) using Sanger sequencing and confirmed the occurrence of key beta variant mutations (K417N, E484K, N501Y) in the S genes of 142 of 153 (93 %) samples. Additionally, one sample had the Y508F mutation and four samples the S477N. Samples with RT-PCR C t scores ranging from 13.85 to 37.47 (mean=25.70) could be Sanger sequenced efficiently. These results show that our method and pipeline can be used to improve the quality of whole-genome assemblies produced using NGS and can be used with any pairs of the most used NGS and Sanger sequencing platforms.

A Performance Analysis of VANETs Propagation Models and Routing Protocols

Vehicular Ad hoc Networks (VANETs) thrive on providing a communication channel between vehicles and infrastructures that facilitate efficient and safe Vehicle-to-Vehicle (V2V) as well as Vehicle-to-Infrastructure (V2I) communications. Reliable and efficient transmission amongst vehicles and Road Side Units (RSUs) is a prime concern of Intelligent Transportation System (ITS). One of the primary challenges involved in vehicular communication is designing an efficient routing mechanism for data dissemination from node to node via a reliable route. The harsh vehicular environment with varied road conditions and obstacles in the signal propagation path induces another challenge. Therefore, this paper presents a comparative analysis of existing routing protocols with propagation models to assist researchers in gaining insight into the existing propagation model and routing protocols. The study also optimizes routing and propagation models for reliable packet dissemination. This work uses a realistic scenario from Open Street Map (OSM), and simulations are performed using SUMO. The trace files generated from SUMO are used for further simulation in NS-3. The simulation results are presented and studied in detail. The results show that the Two-Ray Ground and FRIIS propagation model outperforms the compared models, and the routing protocol OLSR outperforms AODV and DSDV.

Efficient Next-Hop Selection in Multi-Hop Routing for IoT Enabled Wireless Sensor Networks

The Internet of Things (IoT) paradigm allows the integration of cyber and physical worlds and other emerging technologies. IoT-enabled wireless sensor networks (WSNs) are rapidly gaining interest due to their ability to aggregate sensing data and transmit it towards the central or intermediate repositories, such as computational clouds and fogs. This paper presents an efficient multi-hop routing protocol (EMRP) for efficient data dissemination in IoT-enabled WSNs where hierarchy-based energy-efficient routing is involved. It considers a rank-based next-hop selection mechanism. For each device, it considers the residual energy to choose the route for data exchange. We extracted the residual energy at each node and evaluated it based on the connection degree to validate the maximum rank. It allowed us to identify the time slots for measuring the lifetime of the network. We also considered the battery expiry time of the first node to identify the network expiry time. We validated our work through extensive simulations using Network Simulator. We also implemented TCL scripts and C language code to configure low-power sensing devices, cluster heads and sink nodes. We extracted results from the trace files by utilizing AWK scripts. Results demonstrate that the proposed EMRP outperforms the existing related schemes in terms of the average lifetime, packet delivery ratio, time-slots, communication lost, communication area, first node expiry, number of alive nodes and residual energy.

Feature extraction for DNA capillary electrophesis signals based on discrete wavelet transform combined with multi-scale permutation entropy

DNA sequence classification is an important challenge in genomic studies due to non-linear and chaotic behavior of DNA oxidation signals of Adenine, Cytosine, Guanine, and Thymine bases. To achieve genotype identification of samples derived from biological sources accurately, Machine Learning (ML) methods have been commonly preferred instead of expert-based methods due to the ability in handling such these complex-structured biological sequences. Reducing the dimension without sacrificing important information that should not be omitted during the classification process is an important task in ML applications. This study presents a new feature extraction method to detect two sub-types of hepatitis nucleic acid trace files. The proposed method combines both discrete wavelet transform (DWT) and entropy. The DWT decomposes the bases signals up to three levels and thus all necessary information that is hidden in both spatial and frequency domains is aimed to captured. To achieve a good summarization of DNA trace files having different length, multi-scale permutation entropy (MPE) measures are then computed from approximate and detail coefficients of signals stored in the sub-bands. Different feature sets are extracted with the proposed method using real data covering 200 hepatitis DNA trace files and then fed to a simple memory-based learning classifier, k-NN. The classification performance of the proposed feature extraction method is compared against a method based on MPE features without wavelet decomposition. The results indicate, in classifying hepatitis DNA trace files, the average accuracy reaches up to nearly 99% with feature sets based on proposed method even at 30% training samples proportion.

Cyber‐physical network architecture for data stream provisioning in complex ecosystems

AbstractIntelligent fog cyber‐physical social systems (iFog CPSS) is a novel smart city project that uses intrinsic processes to automate microservices such as edge‐to‐fog or fog‐to‐cloud monitoring of complex real‐time activities. This article presents a dynamic cyber‐physical architecture that leverages iFog layers to map location‐based services (LBS) on a spine‐leaf datacenter clos topology. Individual edge clusters are connected to the edge‐fog layer, which communicates with iFog gateways for processing streams' requests. Use‐case application of artificial intelligence (AI) in vehicular ad‐hoc networks (VANETs) is introduced for data stream provisioning. In the validation study, a secure docker‐based iFog CPS experiment is carried out using traffic trace files from C++ modeller. iFog spine‐leaf architecture for fog‐computing and cloud‐computing are compared using two key metrics. For traffic workload utilization, the results show that 83.33% of the traffic workload is utilized at the Fog layer while 16.67% is consumed in the cloud layer. For latency profile, the results indicate that Fog and cloud streams had 20.31% and 77.69%, respectively. In terms of iFog VANET spine‐leaf congestion control, three distinct algorithms are studied, namely the proposed linear routing algorithm (LRA), LEACH, and collection tree protocol (CTP). In each case, the resource utilization for VANET gave 34.45%, 32.18%, and 33.37%, respectively. Latency response gave 11.76%, 82.35%, and 5.89%, respectively. Also, the throughput scenario offered 19.61%, 39.22%, and 41.17%, respectively. Consequently, the iFog scenario offers satisfactory LBS provisioning for VANETs clusters.

Анализ поведения трафика при переходе с одного уровня иерархии сети на другой

The hierarchical network model is the most preferable in the design of computer networks, as it allows you to create a more stable structure of network, rationally allocate available resources, and also provide a higher degree of data protection. In this work, the study of the behavior of the traffic during the transition from one level of the network hierarchy to another, based on the study of the values of the traffic self-similarity degree during this transition. For the study, a simulation model of a computer network with a hierarchical topology was developed using the NS-3 simulator. Also, a window application was developed in the Visual C# programming language. With the help of this application the degree of self-similarity of the traffic was investigated using the files obtained as a result of processing the trace file. Thus, as a result of the study, it can be stated that any changes in the degree of self-similarity of the network traffic when this traffic moves from one level of the hierarchy to another level depends on such a condition as the direction of traffic movement. The initial degree of selfsimilarity of network traffic also effects on the network traffic self-similarity degree.

Encrypted Network Traffic Analysis of Secure Instant Messaging Application: A Case Study of Signal Messenger App

Instant messaging applications (apps) have played a vital role in online interaction, especially under COVID-19 lockdown protocols. Apps with security provisions are able to provide confidentiality through end-to-end encryption. Ill-intentioned individuals and groups use these security services to their advantage by using the apps for criminal, illicit, or fraudulent activities. During an investigation, the provision of end-to-end encryption in apps increases the complexity for digital forensics investigators. This study aims to provide a network forensic strategy to identify the potential artifacts from the encrypted network traffic of the prominent social messenger app Signal (on Android version 9). The analysis of the installed app was conducted over fully encrypted network traffic. By adopting the proposed strategy, the forensic investigator can easily detect encrypted traffic activities such as chatting, media messages, audio, and video calls by looking at the payload patterns. Furthermore, a detailed analysis of the trace files can help to create a list of chat servers and IP addresses of involved parties in the events. As a result, the proposed strategy significantly facilitates extraction of the app’s behavior from encrypted network traffic which can then be used as supportive evidence for forensic investigation.

Service-aware LSP selection with fuzzy based packet scheduling scheme for non-real time traffics

An essential solution is available in Multi-protocol label switching (MPLS), which solve the problems faced by present-day networks: speed, scalability, quality-of-service (QoS) management, and traffic engineering. This paper is an extension of work on Fuzzy based Packet Scheduling Algorithm (FPSA) combined with Packets Processing Algorithm (PPA) in an Internet Protocol/Multi-Protocol Label Switching (IP/MPLS) networks. This will make provision for an intelligent service to the Label Switched Path (LSP) in MPLS networks. Several research work have been proposed on the MPLS Traffic Engineering. However, it is still imperative to further research on the effect of bandwidth increment on the core network using different mechanisms such as the analytical model of MPLS, expert-based packet scheduling algorithm for MPLS QoS support. Since MPLS is not able to provide intelligent routing, it is necessary to propose an intelligent expert system of FPSA combined with PPA. And analytical model of packet forwarding in the MPLS network would be given to provide suitable solution to traffic congestion and reliable services. Furthermore, the network model created using Network Simulator (NS 2), which carries non-real time application such as File Transfer Protocol (FTP) with bandwidth variations. The results obtained from trace files are interpreted by AWK script and used for the further analysis.

An Image Hashing-Based Authentication and Secure Group Communication Scheme for IoT-Enabled MANETs

Mobile ad hoc networks (MANETs) play a highly significant role in the Internet of Things (IoT) for managing node mobility. MANET opens the pathway for different IoT-based communication systems with effective abilities for a variety of applications in several domains. In IoT-based systems, it provides the self-formation and self-connection of networks. A key advantage of MANETs is that any device or node can freely join or leave the network; however, this makes the networks and applications vulnerable to security attacks. Thus, authentication plays an essential role in protecting the network or system from several security attacks. Consequently, secure communication is an important prerequisite for nodes in MANETs. The main problem is that the node moving from one group to another may be attacked on the way by misleading the device to join the neighboring group. To address this, in this paper, we present an authentication mechanism based on image hashing where the network administrator allows the crosschecking of the identity image of a soldier (i.e., a node) in the joining group. We propose the node joining and node migration algorithms where authentication is involved to ensure secure identification. The simulation tool NS-2 is employed to conduct extensive simulations for extracting the results from the trace files. The results demonstrate the effectiveness of the proposed scheme based on the memory storage communication overhead and computational cost. In our scheme, the attack can be detected effectively and also provides a highly robust assurance.

SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models

Due to the rapid growth of the Internet of Things (IoT), applications such as the Augmented Reality (AR)/Virtual Reality (VR), higher resolution media stream, automatic vehicle driving, the smart environment and intelligent e-health applications, increasing demands for high data rates, high bandwidth, low latency, and the quality of services are increasing every day (QoS). The management of network resources for IoT service provisioning is a major issue in modern communication. A possible solution to this issue is the use of the integrated fiber-wireless (FiWi) access network. In addition, dynamic and efficient network configurations can be achieved through software-defined networking (SDN), an innovative and programmable networking architecture enabling machine learning (ML) to automate networks. This paper, we propose a machine learning supervised network traffic classification scheduling model in SDN enhanced-FiWi-IoT that can intelligently learn and guarantee traffic based on its QoS requirements (QoS-Mapping). We capture the different IoT and non-IoT device network traffic trace files based on the traffic flow and analyze the traffic traces to extract statistical attributes (port source and destination, IP address, etc.). We develop a robust IoT device classification process module framework, using these network-level attributes to classify IoT and non-IoT devices. We tested the proposed classification process module in 21 IoT/Non-IoT devices with different ML algorithms and the results showed that classification can achieve a Random Forest classifier with 99% accuracy as compared to other techniques.

Attack Prediction By Using Greedy Algorithm For Diminishing The Drop And Delay In Wireless Sensor Networks

The essential constraint of the internet is that forwarding the data packets of data among the restricted and trustworthy data nodes. If the receiver node is attacker node then it'll drop the data rather than forwarding the data to ensuing neighbor node. Therefore, efficient and secure data transmission is extremely necessary within the network data transmission. Each router node within the network can accept the data packets up to its buffer size only. Once the queue value reached the buffer threshold value then congestion can occur at the node. Once congestion happens then it would lose the data packets. By sending the data packets to the next neighbour node this problem will be resolved. This congestion will be handled by the Fully Distributed Congestion Control FDCC and Cooperative and Memory Efficient Token Bucket (CMTB) algorithms. Because the data is transmitted to the next neighbour node predicting the node behavior is extremely necessary because it is an attacker or the conventional transmitter node because it has to transmit the efficient data securely to the destination node. In this paper, the node behavior will be predicted by analyzing the trace file. The simulation results show that this proposed method would provide a lot of security in data transmission. The WSN comprises a group of sensor nodes that are disseminated on the network. These sensor nodes initially exchange their data packets to the near nodes to send the data packets to the target node. During the transmission of these data packets some data packets drop may also happen inside the network. This packet drop should be kept up as low as feasible for correct data transmission to the target node or destination node. This algorithm highlights the routes with high link quality, low packet delay and with low packet drop. Simulation results show that this proposed algorithm can provide the most effective path for transmitting the data to the destination meanwhile it reduces the packet drop and packet delay.

An efficient branch predictor for improved accuracy of instruction level parallelism

The need for modern processors is based on fast and precise branch predictors to improve the execution of instructions in the pipeline. In a parallel processor, the pipeline cannot execute the conditional instructions with the next clock cycle, leading to a pipeline stall. To address this issue, this paper suggests a variety of branch prediction techniques for improving the execution speed of conditional instructions. Firstly, a simple branch prediction and a dynamic branch prediction are applied to the trace files using saturating counters. Among these two, dynamic branch prediction provides better results by enhancing the accuracy rate of 2.01% than the static branch prediction. Further, the perceptron branch predictor predicts the implementation by using a table of perceptron and train function. This prediction scheme reduces the difficulties in dynamic branch predictor schemes such as reduces the complexity in history length table and improves the accuracy rate by 5.36%. For accuracy, a novel model based on global perceptron branch predictor is developed, which uses both global and per branch information. Trace-driven simulations have been performed by varying the range of hardware budget, traces file size, and the length of history register to increase the accuracy rate of each branch prediction technique. The obtained results suggest that the proposed global perceptron branch predictor provides an increased accuracy rate of 10.47% at 4 kb hardware budget and 8.06% at 4-bit history length than the perceptron branch predictor.

Implementation and comparison of bi-modal dynamic branch prediction with static branch prediction schemes

In a parallel processor, the pipeline cannot fetch the conditional instructions with the next clock cycle, leading to a pipeline stall. So, conditional instructions create a problem in the pipeline because the proper path can only be known after the branch execution. To accurately predict branches, a significant predictor is proposed for the prediction of the conditional branch instruction. Firstly, the always taken prediction scheme and always not-taken prediction scheme are applied to the trace files using a static branch prediction scheme. Further, a bi-modal dynamic branch prediction scheme is proposed, which uses the saturating counter to provide more accuracy rate than the other prediction schemes. The result suggests that the proposed bi-modal dynamic branch prediction scheme provides a higher accuracy rate than always taken and always not-taken static branch prediction by the rate of 9.82% and 6.12%, respectively. Moreover, the proposed bi-modal dynamic branch prediction limits the misprediction rate by 10.33% and 8.16% compared with the always taken and always not-taken static branch prediction scheme.

Estimating Copy-Number Proportions: The Comeback of Sanger Sequencing.

Determination of the relative copy numbers of mixed molecular species in nucleic acid samples is often the objective of biological experiments, including Single-Nucleotide Polymorphism (SNP), indel and gene copy-number characterization, and quantification of CRISPR-Cas9 base editing, cytosine methylation, and RNA editing. Standard dye-terminator chromatograms are a widely accessible, cost-effective information source from which copy-number proportions can be inferred. However, the rate of incorporation of dye terminators is dependent on the dye type, the adjacent sequence string, and the secondary structure of the sequenced strand. These variable rates complicate inferences and have driven scientists to resort to complex and costly quantification methods. Because these complex methods introduce their own biases, researchers are rethinking whether rectifying distortions in sequencing trace files and using direct sequencing for quantification will enable comparable accurate assessment. Indeed, recent developments in software tools (e.g., TIDE, ICE, EditR, BEEP and BEAT) indicate that quantification based on direct Sanger sequencing is gaining in scientific acceptance. This commentary reviews the common obstacles in quantification and the latest insights and developments relevant to estimating copy-number proportions based on direct Sanger sequencing, concluding that bidirectional sequencing and sophisticated base calling are the keys to identifying and avoiding sequence distortions.

ICMetric Security System for Achieving Embedded Devices Security

The interesting with embedded systems is related to the matters of providing privacy and security. There is a need to ensure the integrity and authenticity of the system. The methods such as passwords can be stolen or forgotten therefore the security of any data kept on the system can be exposed by any unauthorised access. In this paper, a new proposal is introduced that exploits Integrated Circuit Metrics (ICMetric) technology to provide identification and improve system security. ICMetric generates device identification based on the features of a device. In this paper, device identification can be generated by using the MEMS sensors, which are gyroscope and magnetometer embedded in many devices. The experiment is based on sensors embedded in intelligent wheelchair. Readings are obtained from the sensors and analysed mathematically and statistically to generate ICMetric number that used to provide identification of the device. ICMetric number is integrated to the dataset extracted from the trace file generated by network simulator version two (ns-2). If the system is attacked, there will be no theft because the ICMetric is not stored on the system and can be regenerated when necessary. The proposed system shows that the using of MEMS sensors to generate ICMetric number increase authentication and security of the system. The simulation results have been compared and studied for high levels of performance metrics and detection capability.

Optimal Reordering Trace Files for Improving Software Testing Suitcase

An invariant can be described as an essential relationship between program variables. The invariants are very useful in software checking and verification. The tools that are used to detect invariants are invariant detectors. There are two types of invariant detectors: dynamic invariant detectors and static invariant detectors. Daikon software is an available computer program that implements a special case of a dynamic invariant detection algorithm. Daikon proposes a dynamic invariant detection algorithm based on several runs of the tested program; then, it gathers the values of its variables, and finally, it detects relationships between the variables based on a simple statistical analysis. This method has some drawbacks. One of its biggest drawbacks is its overwhelming time order. It is observed that the runtime for the Daikon invariant detection tool is dependent on the ordering of traces in the trace file. A mechanism is proposed in order to reduce differences in adjacent trace files. It is done by applying some special techniques of mutation/crossover in genetic algorithm (GA). An experiment is run to assess the benefits of this approach. Experimental findings reveal that the runtime of the proposed dynamic invariant detection algorithm is superior to the main approach with respect to these improvements.