Systematic Studies in Software Product Lines

Marimuthu, C.; Chandrasekaran, K.

doi:10.1145/3106195.3106212

Cited by 17 publications

(11 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, after string calibration (final string) the number of sets of secondary studies 100% retrieved increased to seven. This behavior was observed in the studies of Bano et al [44], Goulão et al [45], Nurdiani et al [46], Hoda et al [47], Marimuthu and C. [48] and Singh et al [23].…”

Section: Search String Validationsupporting

confidence: 59%

Establishing a Search String to Detect Secondary Studies in Software Engineering

Napoleao,

Felizardo,

de Souza

et al. 2019

Preprint

View full text Add to dashboard Cite

Search for secondary studies is essential to establish whether the review on the intended topic has already been done, avoiding waste time. In addition, secondary studies are the inputs of a tertiary study. However, one critical step in searching for secondary studies is to elaborate a search string. The main goal of this work is to analyze search strings to establish directions to better detect secondary studies in Software Engineering (SE). We analyzed seven tertiary studies under two perspectives: (1) structure -strings' terms to detect secondary studies; and (2) field: where searching -titles alone or abstracts alone or titles and abstracts together, among others. We also performed a validation of the results found. The suitable search string for finding secondary studies in SE contain the terms "systematic review", "literature review", "systematic mapping", "mapping study", "systematic map", "meta-analysis", "survey" and "literature analysis". Furthermore, we recommend (1) researchers use the title, abstract and keywords search fields in their searches to increase studies recall; (2) researchers choose carefully their paper title, abstract and keyword terms to increase the chance of having such studies found on digital libraries.

show abstract

Section: Search String Validationsupporting

confidence: 59%

Establishing a Search String to Detect Secondary Studies in Software Engineering

Napoleao,

Felizardo,

de Souza

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…There are other tertiary studies in the literature, each with a different focus such as the process of conducting systematic reviews in software engineering [25] and the quality assessment of systematic reviews in software engineering [26]. There is also a tertiary study in software product lines [27], but it does not give attention to secondary research on requirement patterns.…”

Section: Discussionmentioning

confidence: 99%

Requirement patterns: a tertiary study and a research agenda

2020

View full text Add to dashboard Cite

The low performance of software projects generally arises from erroneous, omitted, misinterpreted, or conflicting requirements. To produce better quality specifications, the practise of requirements reuse through requirement patterns has been widely debated in the secondary literature. However, a tertiary study that provides an overview of secondary studies on the state of the art and the practise of requirement patterns does not exist. This study describes a study of secondary literature on requirement patterns under a perspective on research and practise. The identification and selection methods of secondary studies include automatic search on five sources, inclusion, and exclusion criteria, the snowballing technique, and the quality assessment of those studies. Four secondary studies are considered relevant according to the purpose of this research from a 26-distinct-study group. The authors' contribution is twofold: the tertiary study itself and a preliminary research agenda dealing with state of the art and practise on requirement patterns.

show abstract

“…Twelve SLRs were identified in GSE between 1990-2009, with some of them describing agile practices as an evolving trend. Another study by Marimuthu and Chandrasekaran [106] presents 60 publications on the topic of software product lines between 2008-2016, summarizing their type, quality, authors, publication venue, research topic, and limitations.…”

Section: Tertiary Studies In Sementioning

confidence: 99%

Machine Learning for Software Engineering: A Tertiary Study

2023

View full text Add to dashboard Cite

Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009–2022, covering 6 117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions including: conducting further empirical validation and industrial studies on ML; reconsidering deficient SE methods; documenting and automating data collection and pipeline processes; reexamining how industrial practitioners distribute their proprietary data; and implementing incremental ML approaches.

show abstract

Systematic Studies in Software Product Lines

Cited by 17 publications

References 21 publications

Establishing a Search String to Detect Secondary Studies in Software Engineering

Establishing a Search String to Detect Secondary Studies in Software Engineering

Requirement patterns: a tertiary study and a research agenda

Machine Learning for Software Engineering: A Tertiary Study

Contact Info

Product

Resources

About