Software quality model for a research‐driven organization—An experience report

Wolski, Marcin; Walter, Bartosz; Kupiński, Szymon; Chojnacki, Jakub

doi:10.1002/smr.1911

Cited by 11 publications

(13 citation statements)

References 23 publications

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“…The maintainability metric assessment is performed by SonarQube based on the technical debt ratio, that is the ratio between the effort to develop the application and the effort to fix all maintainability issues presented in the application. SonarQube has been employed in other studies as well within the context of software maintainability and technical debt . More details about the calculation of maintainability can be found in SonarQube's documentation.…”

Section: Resultsmentioning

confidence: 99%

Maintenance process modeling and dynamic estimations based on Bayesian networks and association rules

Chatzimparmpas

Bibi

2019

J Software Evolu Process

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Managing the maintenance process and estimating accurately the effort and duration required for a new release is considered to be a crucial task as it affects successful software project survival and progress over time. In this study, we propose the combination of two well-known machine learning (ML) techniques, Bayesian networks (BNs), and association rules (ARs) for modeling the maintenance process by identifying the relationships among the internal and external quality metrics related to a particular project release to both the maintainability of the project and the maintenance process indicators (ie, effort and duration). We also exploit Bayesian inference, to test the effect of certain changes in internal and external project factors to the maintainability of a project. We evaluate our approach through a case study on 957 releases of five open source JavaScript applications. The results show that the maintainability of a release, the changes observed between subsequent releases, and the time required between two releases can be accurately predicted from size, complexity, and activity metrics. The proposed combined approach achieves higher accuracy when evaluated against the BN model accuracy. KEYWORDSdevelopers' activity, JavaScript, maintainability, maintenance, software quality, source code quality 1 | INTRODUCTION Software quality involves many different activities throughout the operational lifecycle of an application all targeted to the delivery of software that meets the specified requirements and satisfies the evolving needs of end-users. Quality concerns are often intertwined with software maintenance that is closely related to technical debt. 1 Software maintenance is one of the most demanding activities during the software lifecycle, consuming up to 75% of the total project resources, 2 mainly due to the many constraints related to the interdependencies of the source code artifacts of the software under maintenance. According to the IEEE 1219 3 software standards document, software maintenance is defined as the "Modification of a software product after delivery to correct faults, to improve performance or other attributes, or to adapt the product to a modified environment." The maintainability is "the ease of performing maintenance activities," while maintenance effort is subsequently defined as the "effort required to reduce or eliminate maintenance problems." Despite the importance of software maintenance and its crucial effect on the quality of a software application over time, its management and assessment is still an immature discipline in software engineering research and practice. The main problem is that software maintainability (as a single synthesized factor) and maintenance process indicators (ie, effort and number of modifications) are most of the times addressed separately without being able to test the effect of the low level internal and external metrics to both maintainability and maintenance indicators at the same time.This gap can be alleviated by modeling the relationship among t...

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Section: Resultsmentioning

confidence: 99%

Maintenance process modeling and dynamic estimations based on Bayesian networks and association rules

Chatzimparmpas

Bibi

2019

J Software Evolu Process

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“…In the literature of software engineering several quality models have been proposed and gradually evolved such as McCall quality model, Boehm quality model, Dromey quality model, FURPS quality model and ISO 9000 quality model, each model contains different quality characteristics or factors [5][6]. These models have suggested maintenance as a specific type of software products, while maintenance is a spate phase [7].…”

Section: Software Quality Modelsmentioning

confidence: 99%

“…According to Lee [8], McCall mentions maintainability as a factor for software development. Because this model is ancient, there was no consideration for new features of maintenance tools; it has not taken into account the unique characteristics of software maintenance tools [9], [5].…”

Section: Software Quality Modelsmentioning

confidence: 99%

Software Quality Model for Maintenance Software Purposes

Fawareh¹

2020

International Journal of Engineering Research and Technology

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Software maintenance tools developed for attempted to raise the success rate of software systems over the past century. Improve software tools quality models and other software elements to make it more customer satisfaction and achieve customer permanence. Several quality models and variables have proposed to decrease software system failure and complexity. Also, software quality models proposed to assess the general and particular types of software products. These models have proposed to determine the general or particular scopes of software products-none of these quality models concerns the quality of software maintenance tools. The proposed software quality maintenance models developed based on the maintenance tools factor and the comparisons between the well-known quality models. These comparisons are the leakage of criteria based on distinct views and knowledge of maintenance tools requirement. The proposed technique applied to software maintenance tools. The outcome of the proposed technique demonstrates that the twelve factors must deem to increase the quality of software maintenance tools.

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“…In this algorithm we use training database of saved security goals and keywords as the following: 1) Build machine learning classifier using the training database based on NFR classifier for requirements scenario statements. 2) Classify the input sentences into 2 major categories FR/NFR [22][23][24], focusing on NFR with 6 categories: security, efficiency, reliability, functionality, and usability/utility where defined as a quality standards in [24] and [25]. 3) Classify the keywords of security class as (Protect, Confidentiality, Authorized, Authentication, Encryption, Decryption, Confidential and Detection) 4) Convert the resulted set of keywords into set of training/testing features.…”

Section: Algorithm 2: Classifying and Evaluating The Security Featurementioning

confidence: 99%

A novel intelligent model for classify and evaluating non-functional security requirements form scenarios

AbdelQader

2019

IJEECS

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Software requirements with its functional and non-functional methods are the first important phase in producing a software system with free errors. The functional requirements are the visual actions that may easily evaluated from the developer and from the user, but non-functional requirements are not visual and need a lot of efforts to be evaluated. One of the main important non-functional requirements is security, which focuses on generating secure systems from strangers. Evaluating the security of the system in earlier steps will help to reduce the efforts of reveals critical system threats. Security threats found because of leaking of security scenarios in requirement phase. In this paper, we purpose an intelligent model to extract and evaluate security features from scenarios based on set of security system goals and a set of security requirements saved on rich story scenarios dataset (RSSD). This model will used a support vector machine (SVM) classifier to classify the security requirement based on RSS dataset. The using of SVM will enhance the overall process of evaluating the security requirements. The results show a significant enhancement in security improvements.

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Software quality model for a research‐driven organization—An experience report

Cited by 11 publications

References 23 publications

Maintenance process modeling and dynamic estimations based on Bayesian networks and association rules

Maintenance process modeling and dynamic estimations based on Bayesian networks and association rules

Software Quality Model for Maintenance Software Purposes

A novel intelligent model for classify and evaluating non-functional security requirements form scenarios

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