Machine Learning Techniques for Software Maintainability Prediction: Accuracy Analysis

Elmidaoui, Sara; Cheikhi, Laila; Idri, Ali; Abran, Alain

doi:10.1007/s11390-020-9668-1

Cited by 13 publications

(9 citation statements)

References 76 publications

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“…Step 4 Calculate the Euclidean distance between the real and ideal solutions using Eqs. ( 14) and (15).…”

Section: Resultsmentioning

confidence: 99%

“…In 2020, Elmidaoui et al 15 conducted a study on empirical evidence for the accuracy of software product maintainability prediction (SPMP) using ML techniques. The after-effects of about 77 studies that were published between 2000 and 2018 are inspected in this study based on the following criteria: maintainability prediction approaches, validation methods, accuracy criteria, the overall accuracy of ML techniques, and the techniques with the best performance.…”

Section: Recent Workmentioning

confidence: 99%

“…Figure15. Analysis on renowned maintainability prediction models using evaluation measures on PC3 dataset.…”

mentioning

confidence: 99%

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A multiple criteria decision analysis based approach to remove uncertainty in SMP models

Yenduri

Gadekallu

2022

Sci Rep

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Software has to be updated frequently to match the customer needs. If software maintainability is not given priority, it affects the software development life cycle and maintenance expenses, which deplete organizational assets. Before releasing software, maintainability must be estimated, as the impact of bugs and errors can affect the cost and reputation of the organization after deployment. Regardless of the programming paradigm, it’s important to assess software maintainability. Many software maintainability prediction models’ compatibilities with new programming paradigms are criticized because their limited applicability over heterogeneous datasets. Due this challenge small and medium-sized organizations may even skip the maintainability assessment, resulting in huge lose to such organizations. Motivated by this fact, we used Genetic Algorithm optimized Random Forest technique (GA) for software maintainability prediction models over heterogeneous datasets. To find optimal model for software maintainability prediction, the Technique for Order preference by Similarity to Ideal Solution (TOPSIS), a popular multiple-criteria decision-making model, is adopted. From the results, it is concluded that the GA is optimal for predicting maintainability of software developed in various paradigms.

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“…Step 4 Calculate the Euclidean distance between the real and ideal solutions using Eqs. ( 14) and (15).…”

Section: Resultsmentioning

confidence: 99%

Section: Recent Workmentioning

confidence: 99%

See 1 more Smart Citation

A multiple criteria decision analysis based approach to remove uncertainty in SMP models

Yenduri

Gadekallu

2022

Sci Rep

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“…However, some other research studies focused on the use of independent variables (that we detailed in the next question). Thus, several factors can affect the prediction results such as the size of the dataset [55]. Therefore, many research studies have used a single dataset [27].…”

Section: Studymentioning

confidence: 99%

Software Enhancement Effort Prediction Using Machine-Learning Techniques: A Systematic Mapping Study

2021

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Accurate prediction of software enhancement effort is a key success in software project management. To increase the accuracy of estimates, several proposals used machine-learning (ML) techniques for predicting the software project effort. However, there is no clear evidence for determining which techniques to select for predicting more accurate effort within the context of enhancement projects. This paper aims to present a systematic mapping study (SMS) related to the use of ML techniques for predicting software enhancement effort (SEME). A SMS was performed by reviewing relevant papers from 1995 through 2020. We followed well-known guidelines. We selected 30 relevant studies; 19 from journals and 11 conferences proceedings through 4 search engines. Some of the key findings indicate that (1) there is relatively little activity in the area of SEME, (2) most of the successful studies cited focused on regression problems for enhancement maintenance effort prediction, (3) SEME is the dependent variable the most commonly used in software enhancement project planning, and the enhancement size (or the functional change size) is the most used independent variables, (4) several private datasets were used in the selected studies, and there is a growing demand for the use of commonly published datasets, and (5) only single models were employed for SEME prediction. Results indicate that much more work is needed to develop repositories in all prediction models. Based on the findings obtained in this SMS, estimators should be aware that SEME using ML techniques as part of non-algorithmic models demonstrated increased accuracy prediction over the algorithmic models. The use of ML techniques generally provides a reasonable accuracy when using the enhancement functional size as independent variables.

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“…The importance of producing maintainable software is also indicated by the fact that maintenance-related tasks (i.e., code refactoring given the existence of updated requirements or bugs) often require up to 80% of the total effort put into the software project [8]. To that end, several research efforts are directed towards the identification and construction of efficient methodologies along with the respective tools that enable evaluating maintainability [9].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Analytics towards Software Sustainability: The Case of Open-Source Multimedia Tools on Cultural Storytelling

Papamichail

Symeonidis

2021

Sustainability

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The continuous evolution of modern software technologies combined with the deluge of available “ready-to-use” data has triggered revolutionary breakthroughs in several domains, preservation of cultural heritage included. This breakthrough is more than obvious just by considering the numerous multimedia tools and frameworks that actually serve as a means of providing enhanced cultural storytelling experiences (e.g., navigation in historical sites using VR, 3D modeling of artifacts, or even holograms), which are now readily available. In this context and inspired by the vital importance of sustainability as a concept that expresses the need to create the necessary conditions for future generations to use and evolve present artifacts, we target the software engineering domain and propose a systematic way towards measuring the extent to which a software artifact developed and applied in the cultural heritage domain is sustainable. To that end, we present a data-driven methodology that harnesses data residing in online software repositories and involves the analysis of various open-source multimedia tools and frameworks.

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Machine Learning Techniques for Software Maintainability Prediction: Accuracy Analysis

Cited by 13 publications

References 76 publications

A multiple criteria decision analysis based approach to remove uncertainty in SMP models

A multiple criteria decision analysis based approach to remove uncertainty in SMP models

Software Enhancement Effort Prediction Using Machine-Learning Techniques: A Systematic Mapping Study

Data-Driven Analytics towards Software Sustainability: The Case of Open-Source Multimedia Tools on Cultural Storytelling

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