A method for business logic extraction from legacy COBOL code of industrial systems

2016 ◽  
pp. 017-025
Author(s):  
A.A. Guba ◽  
◽  
A.V. Kolchin ◽  
S.V. Potiyenko ◽  
◽  
...  
Keyword(s):  
Test Generation ◽  
Software Tool ◽  
Software Systems ◽  
Software System ◽  
Business Logic ◽  
Industrial Systems

The purpose of this work is to develop a software tool for analysis automation and simplifying of understanding of software systems behavior. Methods for translation, abstraction, debugging and test generation for COBOL are proposed. We developed a software system, which implements the methods.

Research Notes: Binary Test-Suites Using Covering Arrays

2018 ◽  
Vol 28 (09) ◽  
pp. 1321-1337 ◽  
Author(s):  
Jose Torres-Jimenez ◽  
Himer Avila-George ◽  
Ezra Federico Parra-González
Keyword(s):  
Software Testing ◽  
Test Generation ◽  
Software Systems ◽  
Combinatorial Testing ◽  
Covering Array ◽  
Covering Arrays ◽  
Almost All ◽  
Generation Problem ◽  
Test Suites

Software testing is an essential activity to ensure the quality of software systems. Combinatorial testing is a method that facilitates the software testing process; it is based on an empirical evidence where almost all faults in a software component are due to the interaction of very few parameters. The test generation problem for combinatorial testing can be represented as the construction of a matrix that has certain properties; typically this matrix is a covering array. Covering arrays have a small number of tests, in comparison with an exhaustive approach, and provide a level of interaction coverage among the parameters involved. This paper presents a repository that contains binary covering arrays involving many levels of interaction. Also, it discusses the importance of covering array repositories in the construction of better covering arrays. In most of the cases, the size of the covering arrays included in the repository reported here are the best upper bounds known, moreover, the files containing the matrices of these covering arrays are available to be downloaded. The final purpose of our Binary Covering Arrays Repository (BCAR) is to provide software testing practitioners the best-known binary test-suites.

A Comprehensive Assessment Tool for Patient Registry Software Systems: The CIPROS Checklist

2015 ◽  
Vol 54 (05) ◽  
pp. 447-454 ◽  
Author(s):  
U. Mansmann ◽  
D. Lindoerfer
Keyword(s):  
Systematic Review ◽  
Critical Appraisal ◽  
Assessment Tool ◽  
Wide Spectrum ◽  
Patient Registry ◽  
Software Systems ◽  
Software System ◽  
Review Of The Literature ◽  
Patient Registries ◽  
Wide Range

SummaryBackground: Patient registries are an important instrument in medical research. Often their structure is complex and their implementation uses composite software systems to meet the wide spectrum of challenges.Objectives: For the implementation of a registry, there is a wide range of commercial, open source, and self-developed systems available and a minimal standard for the critical appraisal of their architecture is needed.Methods: We performed a systematic review of the literature to define a catalogue of relevant criteria to construct a minimal appraisal standard.Results: The CIPROS list is developed based on 64 papers which were found by our systematic review. The list covers twelve sections and contains 72 items.Conclusions: The CIPROS list supports developers to assess requirements on existing systems and strengthens the reporting of patient registry software system descriptions. It can be a first step to create standards for patient registry software system assessments.

scholarly journals An Integrated Method for Improving Remodularization in Software Systems using Probability Method

2019 ◽  
Vol 8 (9) ◽  
pp. 2662-2668
Keyword(s):  
Clustering Algorithm ◽  
Software Systems ◽  
Probability Method ◽  
Software System ◽  
Integrated Method ◽  
Software Modularization ◽  
Estimation Of Distribution ◽  
Automatic Software ◽  
Structural Connections ◽  
Genetic Clustering

Effective software system must advance to stay pertinent, however this procedure of development can cause the product design to rot and prompt essentially diminished efficiency and even dropped projects. Remodularization tasks can be performed to fix the structure of a software system and evacuate the disintegration brought about by programming advancement. Software remodularization comprises in rearranging software entities into modules to such an extent that sets of substances having a place with similar modules are more comparable than those having a place with various modules.However, re-modularizing systems automatically is challenging in order to enhance their sustainability. In this paper, we have introduced a procedure of automatic software remodularization that helps software maintainers to enhance the software modularization quality by assessing the coupling and attachment among programming components. For precision coupling measures, the proposed technology uses structural coupling measurements. The proposed methodology utilizes tallying of class' part capacities utilized by a given class as a basic coupling measure among classes. The interaction between class files measures structural connections between software elements (classes). In this paper, probability based remodularization (PBR) approach has been proposed to remodularize the software systems. The file ordering process is done by performing probability based approach and remodularization is done based on the dependency strength or connectivity among the files. The proposed technique is experimented on seven software systems. The efficiency is measured by utilizing Turbo Modularization Quality (MQ) that promotes edge weighing module dependence graph (MDG). It very well may be presumed that when comparing performance with the subsisting techniques, for instance, Bunch – GA (Genetic Algorithm), DAGC (Development of Genetic Clustering Algorithm) and Estimation of Distribution Algorithm (EDA), the proposed methodology has greater Turbo MQ value and lesser time complexity with Bunch-GA in the software systems assessed

Requirements Engineering for Software Systems: A Systematic Literature Review

2021 ◽  
Vol 12 (7) ◽  
pp. 339-349
Author(s):  
A. A. Kodubets ◽  
◽  
I. L. Artemieva ◽  
Keyword(s):  
Literature Review ◽  
Requirements Engineering ◽  
Language Processing ◽  
Systematic Literature Review ◽  
Large Scale ◽  
Development Process ◽  
Research Question ◽  
Software Systems ◽  
Software System ◽  
Requirements Development

This article contains a systematic literature review of requirements engineering for software systems. The literature published within last 5 years was included into the review. A research question was defined as requirements development process of large scale software system (with thousands of requirements) and an interaction problem during this process (communication, coordination and control). The problem is caused by the fact that large-scale software system requirements process is a cross-disciplinary task and it involves multiple parties — stakeholders, domain experts, and suppliers with own goals and constrains, and thus, the interaction between them seriously slows down the overall requirements development process than writing the requirements specification itself. The research papers were classified by several research directions: Natural Language Processing for Requirements Engineering (NLP4RE), Requirement Prioritization, Requirements Traceability, Quality of Software Requirements, Non-functional Requirements and Requirements Elicitation. Motivation and intensity of each direction was described. Each direction was structured and represented with the key references. A contribution of each research direction into the research question was analyzed and summarized including potential further steps. It was identified that some researchers had met a part of the described problem in different forms during their researches. At the end, other researches were described additionally in a short overview. To approach the research question further potential direction was described.

Non-Intrusive Adaptation of System Execution Traces for Performance Analysis of Software Systems

2014 ◽  
pp. 473-493
Author(s):  
Manjula Peiris ◽  
James H. Hill
Keyword(s):  
Performance Analysis ◽  
System Performance ◽  
Source Code ◽  
Software Systems ◽  
Software System ◽  
Original Source ◽  
Performance Properties ◽  
Execution Traces ◽  
Execution Trace ◽  
Analysis Of Performance

This chapter discusses how to adapt system execution traces to support analysis of software system performance properties, such as end-to-end response time, throughput, and service time. This is important because system execution traces contain complete snapshots of a systems execution—making them useful artifacts for analyzing software system performance properties. Unfortunately, if system execution traces do not contain the required properties, then analysis of performance properties is hard. In this chapter, the authors discuss: (1) what properties are required to analysis performance properties in a system execution trace; (2) different approaches for injecting the required properties into a system execution trace to support performance analysis; and (3) show, by example, the solution for one approach that does not require modifying the original source code of the system that produced the system execution.

Improvement of Self-Assessment Effectiveness by Activity Monitoring and Analysis

2012 ◽  
pp. 1779-1798
Author(s):  
Dumitru Dan Burdescu ◽  
Marian Cristian Mihaescu
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Input Data ◽  
Software System ◽  
Business Logic ◽  
Self Assessment ◽  
Student Activities ◽  
Learning Platform ◽  
Engineering Department ◽  
E Learning

Self-assessment is one of the crucial activities within e-learning environments that provide learners with feedback regarding their level of accumulated knowledge. From this point of view, the authors think that guidance of learners in self-assessment activity must be an important goal of e-learning environment developers. The scope of the chapter is to present a recommender software system that runs along the e-learning platform. The recommender software system improves the effectiveness of self-assessment activities. The activities performed by learners represent the input data and the machine learning algorithms are used within the business logic of the recommender software system that runs along the e-learning platform. The output of the recommender software system is represented by advice given to learners in order to improve the effectiveness of self-assessment process. The methodology for obtaining improvement of self-assessment is based on embedding knowledge management into the business logic of the e-learning platform. Naive Bayes Classifier is used as machine learning algorithm for obtaining the resources (e.g., questions, chapters, and concepts) that need to be further accessed by learners. The analysis is accomplished for disciplines that are well structured according to a concept map. The input data set for the recommender software system is represented by student activities that are monitored within Tesys e-learning platform. This platform has been designed and implemented within Multimedia Applications Development Research Center at Software Engineering Department, University of Craiova. Monitoring student activities is accomplished through various techniques like creating log files or adding records into a table from a database. The logging facilities are embedded in the business logic of the e-learning platform. The e-learning platform is based on a software development framework that uses only open source software. The software architecture of the e-learning platform is based on MVC (model-view-controller) model that ensures the independence between the model (represented by MySQL database), the controller (represented by the business logic of the platform implemented in Java) and the view (represented by WebMacro which is a 100% Java open-source template language).

Empirical Studies on the Functional Complexity of Software in Large-Scale Software Systems

2011 ◽  
Vol 3 (3) ◽  
pp. 23-42 ◽  
Author(s):  
Yingxu Wang ◽  
Vincent Chiew
Keyword(s):  
Cognitive Complexity ◽  
Large Scale ◽  
Complexity Analysis ◽  
Software Systems ◽  
Development Effort ◽  
Analysis Tool ◽  
Software System ◽  
Time Space ◽  
Functional Complexity ◽  
Scope Problem

Functional complexity is one of the most fundamental properties of software because almost all other software attributes and properties such as functional size, development effort, costs, quality, and project duration are highly dependent on it. The functional complexity of software is a macro-scope problem concerning the semantic properties of software and human cognitive complexity towards a given software system; while the computational complexity is a micro-scope problem concerning algorithmic analyses towards machine throughput and time/space efficiency. This paper presents an empirical study on the functional complexity of software known as cognitive complexity based on large-scale samples using a Software Cognitive Complexity Analysis Tool (SCCAT). Empirical data are obtained with SCCAT on 7,531 programs and five formally specified software systems. The theoretical foundation of software functional complexity is introduced and the metric of software cognitive complexity is formally modeled. The functional complexities of a large-scale software system and the air traffic control systems (ATCS) are rigorously analyzed. A novel approach to represent software functional complexities and their distributions in software systems is developed. The nature of functional complexity of software in software engineering is rigorously explained. The relationship between the symbolic and functional complexities of software is quantitatively analyzed.

scholarly journals AI for Software Quality Assurance Blue Sky Ideas Talk

2020 ◽  
Vol 34 (09) ◽  
pp. 13529-13533
Author(s):  
Meir Kalech ◽  
Roni Stern
Keyword(s):  
Quality Assurance ◽  
Software Quality ◽  
Test Generation ◽  
Machine Learning Techniques ◽  
Software Quality Assurance ◽  
Software Systems ◽  
Automated Test Generation ◽  
Root Cause ◽  
Test Execution ◽  
Learning Techniques

Modern software systems are highly complex and often have multiple dependencies on external parts such as other processes or services. This poses new challenges and exacerbate existing challenges in different aspects of software Quality Assurance (QA) including testing, debugging and repair. The goal of this talk is to present a novel AI paradigm for software QA (AI4QA). A quality assessment AI agent uses machine-learning techniques to predict where coding errors are likely to occur. Then a test generation AI agent considers the error predictions to direct automated test generation. Then a test execution AI agent executes tests, that are passed to the root-cause analysis AI agent, which applies automatic debugging algorithms. The candidate root causes are passed to a code repair AI agent that tries to create a patch for correcting the isolated error.

scholarly journals A Game Theoretic Approach for Quality Assurance in Software Systems Using Antifragility-Based Learning Hooks

2020 ◽  
Vol 22 (3) ◽  
pp. 1-18
Author(s):  
Vimaladevi M. ◽  
Zayaraz G.
Keyword(s):  
Quality Assurance ◽  
Defect Density ◽  
Fault Injection ◽  
Object Oriented ◽  
Quality Parameters ◽  
Software Systems ◽  
Software System ◽  
Theoretic Approach ◽  
Reward Function ◽  
Learning Reinforcement

The use of software in mission critical applications poses greater quality needs. Quality assurance activities are aimed at ensuring such quality requirements of the software system. Antifragility is a property of software that increases its quality as a result of errors, faults, and attacks. Such antifragile software systems proactively accepts the errors and learns from these errors and relies on test-driven development methodology. In this article, an innovative approach is proposed which uses a fault injection methodology to perform the task of quality assurance. Such a fault injection mechanism makes the software antifragile and it gets better with the increase in the intensity of such errors up to a point. A software quality game is designed as a two-player game model with stressor and backer entities. The stressor is an error model which injects errors into the software system. The software system acts as a backer, and tries to recover from the errors. The backer uses a cheating mechanism by implementing software Learning Hooks (SLH) which learn from the injected errors. This makes the software antifragile and leads to improvement of the code. Moreover, the SLH uses a Q-Learning reinforcement algorithm with a hybrid reward function to learn from the incoming defects. The game is played for a maximum of K errors. This approach is introduced to incorporate the anti-fragility aspects into the software system within the existing framework of object-oriented development. The game is run at the end of every increment during the construction of object-oriented systems. A detailed report of the injected errors and the actions taken is output at the end of each increment so that necessary actions are incorporated into the actual software during the next iteration. This ensures at the end of all the iterations, the software is immune to majority of the so-called Black Swans. The experiment is conducted with an open source Java sample and the results are studied selected two categories of evaluation parameters. The defect related performance parameters considered are the defect density, defect distribution over different iterations, and number of hooks inserted. These parameters show much reduction in adopting the proposed approach. The quality parameters such as abstraction, inheritance, and coupling are studied for various iterations and this approach ensures considerable increases in these parameters.

An Empirical Study on the Architecture Instability of Software Projects

2019 ◽  
Vol 29 (04) ◽  
pp. 515-545 ◽  
Author(s):  
Lerina Aversano ◽  
Daniela Guardabascio ◽  
Maria Tortorella
Keyword(s):  
Empirical Study ◽  
Programming Languages ◽  
Quantitative Information ◽  
Software Systems ◽  
Software Project ◽  
Software System ◽  
Software Projects ◽  
System Maturity ◽  
Multiple Releases ◽  
Initial Concept

Software architecture is an artifact that expresses how the initial concept of a software system has actually been implemented. However, changes to the requirement imply continuous modification of the software system and may affect its architecture. It is expected that when a software system reaches the mature state, the requirements for evolution decrease and its architecture becomes more stable. The paper analyzes how the architecture of a software system evolves during its life cycle, with the aim of obtaining quantitative information on its possible instability after it has been declared mature. The goal is to verify if the architectural instability decreases with the increase of the software system maturity and to identify the software components that are more unstable among multiple releases. The paper proposes metrics that measure the instability of the architecture of a software system and its components through different releases. Open source software projects classified as mature and active and related historical data are analyzed. The results of the empirical study point out that the instability of software projects continues to evolve even after they are declared mature. The proposed metrics give a useful support for investigating the instability of a software project, even if further factors can be analyzed. Furthermore, the study can be replicated on other software systems belonging to different domains and developed using different programming languages.

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