Software quality improvement: a model based on managing factors impacting software quality

Janicijevic, Ivan; Krsmanovic, Maja; Zivkovic, Nedeljko; Lazarević, Saša

doi:10.1007/s11219-014-9257-z

Cited by 9 publications

(15 citation statements)

References 40 publications

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“…Our study based on the analysis of commits and committers is in line with the investigation of change control standards of that study. [Janicijevic, Krsmanovic et al, 2016] proposed a markovian decision system that models the "stochastic processes of a quality management system and selection of the optimum set of factors impacting software quality". Among other factors, this research evaluates how programmer skills and software development methods impact the customer requirements fulfilment.…”

Section: Project Influence On Software Qualitymentioning

confidence: 99%

“…Code quality has been widely investigated in the literature and has been recognized as one of the most significant factors with a direct impact in competitiveness in the software development industry [Abrahao, Baldassarre et al, 2016, Baggen, Correia et al, 2012, Janicijevic, Krsmanovic et al, 2016. Some consequences generated by inadequate levels of quality are, for example, poor designs that lead to systems that are difficult to be maintained and extended [Schranz, Schindler et al, 2019], systems delivered with many defects resulting in a high dissatisfaction of end-users [Maxim and Kessentini, 2016], many dead or duplicated code that dramatically increase the maintenance and evolution cost [Perez-Castillo, Piattini et al, 2018], among many other harmful effects.…”

Section: Introductionmentioning

confidence: 99%

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Understanding the Impact of Development Efforts in Code Quality

Pérez‐Castillo¹,

Piattini²

2021

jucs

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Today, there is no company that does not attempt to control or assure software quality in a greater or lesser extent. Software quality has been mainly studied from the perspectives of the software product and the software process. However, there is no thorough research about how code quality is affected by the software development projects’ contexts. This study analyses how the evolution of the development effort (i.e., the number of developers and their contributions) influences the code quality (i.e., the number of bugs, code smells, cloning, etc). This paper presents a multiple case study that analyses 13 open-source projects from GitHub and SonarCloud, and retrieves more than 95,000 commits and more than 25,000 quality measures. The insights are that more developers or higher number of commits does not necessary influence worse quality levels. After applying a clustering algorithm, it is detected an inverse correlation in some cases where specific efforts were made to improve code quality. The size of commits and the relative weight of developers in their teams might also affect measures like complexity or cloning. Project managers can therefore understand the mentioned relationships and consequently make better decisions based on the information retrieved from code repositories.

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Section: Project Influence On Software Qualitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding the Impact of Development Efforts in Code Quality

Pérez‐Castillo¹,

Piattini²

2021

jucs

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“…By applying the gradient descent function, the ensemble classifier minimizes the out of sample error for achieving higher classification accuracy. From (8), indicates a gradient descent function, is the argument of minimum, is an out of sample error of weak learners . The final output of ensemble classification results lessen the error rate and enhance the classification results.…”

Section: (3)mentioning

confidence: 99%

“…But the error rate of defect prediction was not effectively minimized. A Markov chain and a systematic framework were developed in [8] for software quality analysis by identifying the defects. But the framework failed to perform the feature selection.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Embedded Probit Regressive Reweight Boost Classifier for Software Quality Examination

2019

IJRTE

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In software development, Software quality analysis plays a considerable process. Through the software testing, the quality analysis is performed for efficient prediction of defects in the code. Due to the complicated structure of software projects, code examination has become a demanding issue that has to be addressed at the initial stage of testing for achieving the quality improved results. In order to resolve these issues, the Stochastic Gaussian Neighbor Embedding based Probit Regressive Reweight Boost Classification (SGNE-PRRBC) is introduced for accurate quality prediction system through code examination proficient system. The SGNE-PRRBC technique considers the number of program files as input for software quality analysis through feature selection and classification. Initially, the number of program files is taken from the dataset (DS). After collecting the files, the Gaussian distributive stochastic neighbor embedding technique choose the features (i.e. code metrics) based on the distance similarity. With the assist of Pearson correlative probit regressed reweight boost technique, the classification of program files is performed. The boosting algorithm creates ‘m’ number of weak classifiers i.e. Pearson correlative probit regression to categorize the input program files as normal or defected through analyze the source codes and chosen metrics. After that, the weak learners results are combined into strong through minimizing the out of sample error with gradient descent function. This enhances the accuracy of quality prediction and lessens the false positive rate (FPR). Experimental analysis is performed with various metrics namely accuracy, FPR and computation time (CT) with number of program files. Experimental results evident that the SGNE-PRRBC technique achieves better performance in terms of accuracy, CT and FPR as compared to the conventional methods.

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“…The quality of code, and that of software in general, has been studied extensively, since it is recognized to be very significant as regards achieving competitiveness in the software industry . One key factor that affects code quality is code cloning .…”

Section: Introductionmentioning

confidence: 99%

An empirical study on how project context impacts on code cloning

Pérez‐Castillo

Piattini

2018

J Software Evolu Process

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Code cloning can seriously affect software quality. Code clones are various fragments of syntactically or semantically equivalent code. Some authors argue that code clones have a negative impact on maintainability and understandability, since clones propagate defects and make it mandatory to pay attention to several copies. However, other authors believe clones are not necessarily bad, since self‐admitted clones favor system stability and allow developers to move projects forward. Although some root causes and effects of cloning have been widely studied, there is not much relevant work analyzing how certain projects context factors impact on code cloning. This work presents an empirical validation of six open source projects by considering certain factors from Git repositories measured throughout a total of 70 releases for the 6 systems. The factors analyzed were the number of commits and committers per release, the average size of the commits and the size of the system in each release. The main conclusion obtained from the study is that, while the number of commits and committers and the system size do not significantly affect cloning, larger commits lead to a higher cloning ratio. These insights contribute to predicting and preventing code cloning, thus enabling a software quality improvement.

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Software quality improvement: a model based on managing factors impacting software quality

Cited by 9 publications

References 40 publications

Understanding the Impact of Development Efforts in Code Quality

Understanding the Impact of Development Efforts in Code Quality

Stochastic Embedded Probit Regressive Reweight Boost Classifier for Software Quality Examination

An empirical study on how project context impacts on code cloning

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