A systematic literature review on empirical studies towards prediction of software maintainability

Execution tracing is a tool used in the course of software development and software maintenance to identify the internal routes of execution and state changes while the software operates. Its quality has a high influence on the duration of the analysis required to locate software faults. Nevertheless, execution tracing quality has not been described by a quality model, which is an impediment while measuring software product quality. In addition, such a model needs to consider uncertainty, as the underlying factors involve human analysis and assessment. The goal of this study is to address both issues and to fill the gap by defining a quality model for execution tracing. The data collection was conducted on a defined study population with the inclusion of software professionals to consider their accumulated experiences; moreover, the data were processed by genetic algorithms to identify the linguistic rules of a fuzzy inference system. The linguistic rules constitute a human-interpretable rule set that offers further insights into the problem domain. The study found that the quality properties accuracy, design and implementation have the strongest impact on the quality of execution tracing, while the property legibility is necessary but not completely inevitable. Furthermore, the quality property security shows adverse effects on the quality of execution tracing, but its presence is required to some extent to avoid leaking information and to satisfy legal expectations. The created model is able to describe execution tracing quality appropriately. In future work, the researchers plan to link the constructed quality model to overall software product quality frameworks to consider execution tracing quality with regard to software product quality as a whole. In addition, the simplification of the mathematically complex model is also planned to ensure an easy-to-tailor approach to specific application domains.

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“…Malhotra and Lata outline the machine learning approaches used for software maintainability in [108].…”

Section: Adaptive Methods In Quality Modellingmentioning

confidence: 99%

Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

2021

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“…The arrangement, organization, and formation of the systematic literature review (SLR) were implemented by the methodology chosen by the authors [5]. The framework of this SLR was also based on the protocols followed by the authors [6][7][8]. After constructing the review methodology, a sequence of steps was carried out for this study.…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

“…Figure 4 shows many studies that have encountered the particular diseases present in citrus fruits. The most widespread defects according to count are surface defects (17), P. digitatum and other fungus infections (14), canker (12), melanose (10), HLB (9), and blackspot (7). Table 2 provides a brief introduction to each disease, along with relevant references.…”

Section: Rq1: What Kinds Of Diseases Affect Citrus Fruits?mentioning

confidence: 99%

Image Acquisition, Preprocessing and Classification of Citrus Fruit Diseases: A Systematic Literature Review

Dhiman¹,

Kaur

Balasaraswathi

et al. 2023

Sustainability

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Different kinds of techniques are evaluated and analyzed for various classification models for the detection of diseases of citrus fruits. This paper aims to systematically review the papers that focus on the prediction, detection, and classification of citrus fruit diseases that have employed machine learning, deep learning, and statistical techniques. Additionally, this paper explores the present state of the art of the concept of image acquisition, digital image processing, feature extraction, and classification approaches, and each one is discussed separately. A total of 78 papers are selected after applying primary selection criteria, inclusion/exclusion criteria, and quality assessment criteria. We observe that the following are widely used in the selected studies: hyperspectral imaging systems for the image acquisition process, thresholding for image processing, support vector machine (SVM) models as machine learning (ML) models, convolutional neural network (CNN) architectures as deep learning models, principal component analysis (PCA) as a statistical model, and classification accuracy as evaluation parameters. Moreover, the color feature is the most popularly used feature for the RGB color space. From the review studies that performed comparative analyses, we find that the best techniques that outperformed other techniques in their respective categories are as follows: SVM among the ML methods, ANN among the neural network networks, CNN among the deep learning methods, and linear discriminant analysis (LDA) among the statistical techniques.This study concludes with meta-analysis, limitations, and future research directions.

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“…In 2020, Malhotra et al 18 implemented several ML, statistical (ST) and hybridization (HB) techniques to create prediction models for software maintainability in this work. The important finding is that ML-based models outperform ST models in terms of overall performance.…”

Section: Recent Workmentioning

confidence: 99%

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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A systematic literature review on empirical studies towards prediction of software maintainability

Cited by 9 publications

References 89 publications

Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

Image Acquisition, Preprocessing and Classification of Citrus Fruit Diseases: A Systematic Literature Review

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

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