Data Mining Practical Machine Learning Tools and Techniques

The software inevitably encounters the crash, which will take developers a large amount of effort to find the fault causing the crash (short for crashing fault). Developing automatic methods to identify the residence of the crashing fault is a crucial activity for software quality assurance. Researchers have proposed methods to predict whether the crashing fault resides in the stack trace based on the features collected from the stack trace and faulty code, aiming at saving the debugging effort for developers. However, previous work usually neglected the feature preprocessing operation towards the crash data and only used traditional classification models. In this paper, we propose a novel crashing fault residence prediction framework, called ConDF, which consists of a consistency based feature subset selection method and a state-of-the-art deep forest model. More specifically, first, the feature selection method is used to obtain an optimal feature subset and reduce the feature dimension by reserving the representative features. Then, a simplified deep forest model is employed to build the classification model on the reduced feature set. The experiments on seven open source software projects show that our ConDF method performs significantly better than 17 baseline methods on three performance indicators.

Predicting Crash Fault Residence via Simplified Deep Forest Based on A Reduced Feature Set

It is popular to use software defect prediction (SDP) techniques to predict bugs in software in the past 20 years. Before conducting software testing (ST), the result of SDP assists on resource allocation for ST. However, DP usually works on fine-level tasks (or white-box testing) instead of coarse-level tasks (or black-box testing). Before ST or without historical execution information, it is difficult to get resource allocated properly. Therefore, a SDP-based approach, named DPAHM, is proposed to assist on arranging resource for coarse-level tasks. The method combines analytic hierarchy process (AHP) and variant incidence matrix. Besides, we apply the proposed DPAHM into a proprietary software, named MC. Besides, we conduct an up-to-down structure, including three layers for MC. Additionally, the performance measure of each layer is calculated based on the SDP result. Therefore, the resource allocation strategy for coarse-level tasks is gained according to the prediction result. The experiment indicates our proposed method is effective for resource allocation of coarse-level tasks before executing ST.

Can Defect Prediction Be Useful for Coarse-Level Tasks of Software Testing?

In software defect prediction, unbalanced data poses a substantial obstacle due to the considerably lesser count of corrupted instances in comparison to non-damaged instances. When developing precise defect prediction models in such circumstances, the selection of features must be executed with extreme precision. Nevertheless, knowledge regarding feature selection techniques specifically developed for unbalanced data in the situation regarding the prediction of software errors prediction remains restricted. We combed through pertinent studies from the IEEE Xplore, Springer, Elsevier, Google Scholar, MECS Press, and Wiley databases as part of a systematic literature review. The objective of this investigation was to investigate and assess a range of feature selection methods that have been modified to function with unbalanced data in the domain of software defect prediction. Furthermore, we conducted an analysis of the accuracy, precision, recall, F1 score, and AUC-ROC performance metrics in order to evaluate the efficacy of the feature selection methods. From 2017 to 2023, the review assessed the twenty-one most pertinent articles from twelve periodicals and nine conference proceedings. Based on the comprehensive analysis, it can be inferred that the effectiveness of the prediction model is enhanced by the major studies that were chosen because of the feature selection approaches they used. Chi-Square is an extensively observed feature selection technique. It has been reported that the accuracy of predictions can be enhanced by ensemble models as opposed to a singular model or method. Nonetheless, the application of this method to software defect prediction remains restricted.

A Survey on Feature Selection in Imbalanced Data for Software Defect Prediction

The embodiment of the invention provides a method for identifying and cracking a fragile hash function for firmware of intelligent equipment, which mainly comprises the following steps of: preprocessing the firmware to obtain a binary file to be analyzed; extracting common characteristics of the fragile hash function which is not influenced by the architecture and the compiling optimization options or is influenced by the architecture and the compiling optimization options to be smaller than a preset threshold value; carrying out numerical processing on the features, training and testing the feature data, constructing a reliable neural network model based on logistic regression, and carrying out firmware fragile hash function identification and positioning based on a structured matching method; structural division and extraction are conducted on codes of the fragile hash function, machine codes or assembly codes are converted into VEX IR statements, a Z3 SMT solving expression executedbased on symbols is constructed, solving constraint conditions are added, a collision value is cracked reversely, and whether the collision value is correct or not is verified. The method has the advantages of being low in false alarm rate, accurate in positioning and high in cracking rate for the fragile hash function of the firmware.

Method for identifying and cracking a fragile hash function for firmware of intelligent equipment

Software fault prediction (SFP) is an active issue in software engineering (SE). At present, machine learning (ML) has been successfully applied to SFP classification problems. However, one of the challenges for building software fault prediction models (SFPM) is processing high dimensional datasets, which include many irrelevant and redundant features. To address this issue, feature selection techniques, mainly contain wrapper methods and filter methods, are used. In the paper, we report an empirical study aimed at providing a novel approach to select feature for SFP.

A Novel Feature Selection Method for Software Fault Prediction Model

The invention discloses a feature code generating method facing to a binary file of key software, which includes steps of firstly, dividing a target binary file into two binary sub-files; secondly, respectively solving 32 bits of lowercase MD5 code of the target binary file and two binary sub-files; thirdly, cutting out the post two bits of three 32 bits of lowercase MD5 codes in step two, and combining together according to the order of 'a binary sub-file 1-binary sub-file 2-target binary sub-file', and connecting previous three digits with the last three digits by a separator ' '; finally, generating a six-bit 16 system number as the feature code of the target binary file. The method is simple and clear, and easy to realize while can guarantee the safety; the feature code generating method can effectively improve the working efficiency of the loading management of the binary file and guaranteeing its safety.

Feature code generating method facing to binary file of key software

The invention discloses an improved feature filtering method and device based on correlation feature selection. The method aims at a classification task in software defect prediction, calculates the similarity between the proportions of the features selected by using the CFS method and the proportions of the features in the original data set; the first K features are selected through sorting; theproblem that a filtering method in feature selection is high in efficiency but a prediction model is poor in performance is solved, data preprocessing guidance on a software prediction model is achieved, and the requirements for preprocessing data and improving the data quality under the condition that data is sufficient and irrelevant features or redundant features exist in the data are met; andon the other hand, the method is simple, easy to implement and short in running time, and the constructed software defect prediction model is high in accuracy.

Improved feature filtering method and device based on correlation feature selection

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A Novel Feature Selection Method for Software Fault Prediction Model

Feature code generating method facing to binary file of key software

Improved feature filtering method and device based on correlation feature selection