While working on computers, people frequently confront with various kinds of problems, those beyond their extensive expertise. Microsoft Windows is the widely used Operating System running on numerous personal computers and the reason which gives more irritating problems that require to be addressed. Currently, troubleshooting is considered as a costly and time-consuming approach. The SAITA is an Artificial Intelligent Troubleshooting Agent that utilizes natural language generation, machine learning, and dependency resolving and ontology-based methodologies for solving most common Windows-specific issues within a short period of time than the traditional approach. The assistant learns from the accessible data and accomplishes the task for users as performed by human experts. The main objective of this exploration venture is to distinguish the constraints of existing troubleshooting software and create an AI troubleshooting assistant to provide solutions to fix the identified user issues. The use of this assistant would be economical as an IT help desk alternative in the industry. SAITA is developed to serve as a representative troubleshooter for fundamental user issues, service issues, application issues, and perform environment setup by analyzing software. This system will be able to solve the common Windows user's issues as same as a human with less time.

Smart Intelligent Troubleshooter to Solve Windows Operating System Specific Issues

The software is essentially replacing the human to carry out certain behavior,software's trust mainly displays in trust for its behavior,assessing the trust by the information of software runtime behavior will be more direct and objective. In order to promote the accuracy in the recognition of software runtime behavior,we describes a method based on pattern matching,depositing the definitude behavior patterns to the Knowledge Base. As recognizing the software behavior,match the current monitoring information with The behavior patterns to get the matching degree,This paper describes the integrated process of the recognition of software runtime behavior based on pattern matching. At last,validates the feasibility and accuracy in the experimental system.

A approach for distributed software run-time behavior recognition oriented trust

Multi Robot System (MRS) is one of the most important research areas in the field of Robotics and Artificial Intelligence. The study of Multi Robot Systems may take many aspects; therefore, it is useful to study the Multi Robot Systems from a specific point of view to get a more focused idea. In this paper, we present a review of the recent trends in Multi Robot Systems research by focusing at the collaborative aspect. Furthermore, we address the structure of Multi Robot Systems, their applications and the techniques and algorithms used in the collaborative MRS.

Collaboration in multi-robot systems

Regression testing performed to provide confidence on the newly or updated software system is a resource consuming process. To ease this process, various techniques are developed. One such technique, test case prioritization, orders test cases with respect to the goals such that the most important test case in achieving those goals is scheduled earlier during the testing session. Among such performance goals, the rate of faults detections, measure how faults are detected quickly throughout the regression testing process. Improved dependency detection among faults provides faster feedback to the developers which gives chance to debug leading faults earlier in time. One other goal, the rate of fault severity detection, measure how fast severe fault can be detected in the testing process. Although, previous works address these issues but assumed that the costs of executing test cases and severities of detected faults are the same. However, costs of test and severities of faults varied. Furthermore, they did not consider incorporating evolution process such as applying genetic algorithms to their technique. In this work, we proposed an evolutionary cost-cognizant regression testing approach that prioritizes test case according to the rate of severity detection of test cases connected with dependent faults using genetic algorithms. The aim is to reveal more severe leading faults earlier using least cost in executing the test suite and to measure the efficacy of the technique using APFDc.  

/pdf/evolutionary-cost-cognizant-regression-test-prioritization-2lcq93nisw.pdf

Evolutionary Cost Cognizant Regression Test Prioritization for Object-Oriented Programs Based on Fault Dependency

Software comprehension can be extremely time-consuming due to the ever-growing size of codebases. Consequently, there is an increasing need to accelerate the code comprehension process to facilitate maintenance and reduce associated costs. A crucial aspect of this process is understanding and preserving the high quality of the code dependency structure. While a variety of code structure models already exist, there is a surprising lack of models that closely represent the source code and focus on software comprehension. As a result, there are no readily available and easy-to-use tools to assist with dependency comprehension, refactoring, and quality monitoring of code. To address this gap, we propose the Semantic Code Graph (SCG), an information model that offers a detailed abstract representation of code dependencies with a close relationship to the source code. We establish the critical properties of the SCG model and demonstrate its implementation for Java and Scala languages. To validate the SCG model’s usefulness in software comprehension, we compare it to nine other source code representation models. Additionally, we select 11 well-known and widely-used open-source projects developed in Java and Scala and perform a range of software comprehension activities on them using three different code representation models: the proposed SCG, the Call Graph, and the Class Collaboration Network. We then qualitatively analyze the results to compare the performance of these models in terms of software comprehension capabilities. These activities encompass project structure comprehension, identifying critical project entities, interactive visualization of code dependencies, and uncovering code similarities through software mining. Our findings demonstrate that the SCG enhances software comprehension capabilities compared to the prevailing Class Collaboration Network and Call Graph models. Moreover, the SCG-based data analysis yields actionable software comprehension insights. We also release an open-source tool, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scg-cli , to assist with result reproduction and further research. We believe that the work described is a step towards the next generation of tools that streamline code dependency comprehension and management.

Semantic Code Graph – an information model to facilitate software comprehension

There are many remarkable methods used in software semantics analysis. However, people find some of the methods generally have the common problem of high time complexity or inaccurate results. In order to solve these problems, we propose a program controlled flow algorithm which is based on a control dependency graph and an abstract syntax tree. This algorithm uses object program equivalent substitution and a procedure of dependency graph to replace procedure System Dependency Graph (SDG), which improves the building process of traditional system dependency graph. Experimental results show that the proposed algorithm can effectively reduce the complexity of constructional SDG and increase the rate of program slicing.

System Dependency Graph Construction Algorithm Based on Equivalent Substitution

This existing multi-robot task allocation mechanisms treat tasks as simple, indivisible entities. Also in many applications, the dynamicity and complexity of the environment are not considered. In order to allocate the robots adaptively and flexibility according to the complex task and environment, this paper presents a multi-robot coordination approaches based on virtual pheromone which is inspired by the ant colony foraging behavior in the background of exploring unknown environments by multi-robot system. Numerical and experiment results show that this approach is helpful and reliable, and it is proved that the robots collaboration degree was obviously improved. In addition, the exploration efficiency was greatly improved.

Self-Organizing Task Allocation of Multi-robot

Recently computer systems' call sequences are considered as a data source, this paper expounds how to use Hidden Markov Models (HMM) for software behavior recognition and trend prediction. Due to that HMM is sensitive to initial parameters, especially sensitive to B-parameter which makes model fall into a local optimum in training, this paper proposes using Genetic Algorithm (GA) approach to optimize the B-parameter together with HMM for establishing an optimal training model. The model is called GA-HMM. In order to eliminate the HMM's reflection on observations characteristics, this paper puts forward a new approach to recognize software behavior with hidden states.

The Research of Software Behavior Recognition and Trend Prediction Method Based on GA-HMM

For the HMM exists defects in application in the aspect of software behavior prediction, namely, HMM could trap into local optimization because of the problem of B-parameter, which results in the decrease of HMM’s precision. This paper builds a new model HMM-ACO through combining Ant Colony Optimization (ACO) algorithm with HMM, with system calls as the data source, improving the prediction accuracy rate of HMM. In order to eliminate the HMM’s reflection on observations characteristics, this paper puts forward a new approach to recognize software behavior with hidden states.

The Software Behavior Trend Prediction Based on HMM-ACO

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Papers

System Dependency Graph Construction Algorithm Based on Equivalent Substitution

Self-Organizing Task Allocation of Multi-robot

The Research of Software Behavior Recognition and Trend Prediction Method Based on GA-HMM

The Software Behavior Trend Prediction Based on HMM-ACO