Comparing Reuse Strategies in Different Development Environments

Varnell-Sarjeant, Julia; Andrews, Anneliese

doi:10.1016/bs.adcom.2014.10.002

Cited by 9 publications

(5 citation statements)

References 66 publications

(87 reference statements)

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“…Varnell et al [21] performed a survey of software reuse, comparing reuse outcomes for embedded systems versus non-embedded systems using different development approaches. They selected 84 candidate studies and classified them by study type, system type (embedded and non-embedded) and development approach.…”

Section: Related Workmentioning

confidence: 99%

What software reuse benefits have been transferred to the industry? A systematic mapping study

Barros-Justo

Pinciroli

Matalonga

et al. 2018

Information and Software Technology

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Context: The term software reuse was first used in 1968 at the NATO conference. Since then, work in the scientific literature have stated that the application of software reuse offers benefits such as increase in quality and productivity. Nonetheless, in spite of many publications reporting software reuse experiences, evidence that such benefits having reached industrial settings is scarce. Objective: To identify and classify the benefits transferred to real-world settings by the application of software reuse strategies. Method: We conducted a systematic mapping study (SMS). Our search strategies retrieved a set of 2,413 papers out of which 49 were selected as primary studies. We defined five facets to classify these studies: a) the type of benefit, b) the reuse process, c) the industry's domain, d) the type of reuse and e) the type of research reported. Results: Quality increase (28 papers) and Productivity increase (25 papers) were the two most mentioned benefits. Component-Based Development (CBD) was the most reported reuse strategy (41%), followed by Software Product Lines (SPL, 30%). The selected papers mentioned fourteen industrial domains, of which four stand out: aerospace and defense, telecommunications, electronics and IT services. The application of systematic reuse was reported in 78% of the papers. Regarding the research type, 50% use evaluation research as the investigation method. Finally, 13 papers (27%) reported validity threats for the research method applied. Conclusions: The literature analyzed presents a lack of empirical data, making it difficult to evaluate the effective transfer of benefits to the industry. This work did not find any relationship between the reported benefits and the reuse strategy applied by the industry or the industry domain. Although the most reported research method was industrial case studies (25 works), half of these works (12) did not report threats to validity.

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Section: Related Workmentioning

confidence: 99%

What software reuse benefits have been transferred to the industry? A systematic mapping study

Barros-Justo

Pinciroli

Matalonga

et al. 2018

Information and Software Technology

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“…Their major research focused on detecting design flaws at higher level of granularity. Julia et al 25 in 2015 compared four software refuse strategies for embedded and nonembedded systems. The researchers concluded that reuse of embedded system code does not benefit much.…”

Section: Related Workmentioning

confidence: 99%

Software reuse analytics using integrated random forest and gradient boosting machine learning algorithm

Sandhu

Batth

2020

Softw Pract Exp

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The term Cleaner Production (CP) for Production Companies is contemplated as influential to get sustainable production. CP mainly deals with three R's that is, reuse, reduce, and recycle. For software enterprise, the software reuse plays a pivotal role. Software reuse is a process of producing new products or software from the existing software by updating it. To extract useful information from the existing software data mining comes into light. The algorithms used for software reuse face issues related to maintenance cost, accuracy, and performance. Also, the currently used algorithm does not give accurate results on whether the component of software can be reused. Machine Learning gives the best results to predicate if the given software component is reusable or not. This paper introduces an integrated Random Forest and Gradient Boosting Machine Learning Algorithm (RFGBM) which test the reusability of the given software code considering the object‐oriented parameters such as cohesion, coupling, cyclomatic complexity, bugs, number of children, and depth inheritance tree. Further, the proposed algorithm is compared with J48, AdaBoostM1, LogitBoost, Part, One R, LMT, JRip, DecisionStump algorithms. Performance metrices like accuracy, error rate, Relative Absolute Error, and Mean Absolute Error are improved using RFGBM. This algorithm also utilizes data preprocessing with the help of an unsupervised filter to remove the missing value for efficiency improvement. Proposed algorithm outperforms existing in term of performance parameters.

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“…Under the umbrella of this broad definition, several terms coexist, such as Component-Based Development (CBD), Software Product Lines (SPL), Model-Driven Development (MDD), Domain Engineering (or Domain Analysis) and Commercial-Of-The-Shelf (COTS) among others [3]. There are also two main approaches to development: with-reuse (development using pre-existing components) and for-reuse (development of reusable components) [4].…”

Section: Accepted Manuscriptmentioning

confidence: 99%