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Erschienen in: Empirical Software Engineering 6/2017

06.01.2017

On the pragmatic design of literature studies in software engineering: an experience-based guideline

verfasst von: Marco Kuhrmann, Daniel Méndez Fernández, Maya Daneva

Erschienen in: Empirical Software Engineering | Ausgabe 6/2017

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Abstract

Systematic literature studies have received much attention in empirical software engineering in recent years. They have become a powerful tool to collect and structure reported knowledge in a systematic and reproducible way. We distinguish systematic literature reviews to systematically analyze reported evidence in depth, and systematic mapping studies to structure a field of interest in a broader, usually quantified manner. Due to the rapidly increasing body of knowledge in software engineering, researchers who want to capture the published work in a domain often face an extensive amount of publications, which need to be screened, rated for relevance, classified, and eventually analyzed. Although there are several guidelines to conduct literature studies, they do not yet help researchers coping with the specific difficulties encountered in the practical application of these guidelines. In this article, we present an experience-based guideline to aid researchers in designing systematic literature studies with special emphasis on the data collection and selection procedures. Our guideline aims at providing a blueprint for a practical and pragmatic path through the plethora of currently available practices and deliverables capturing the dependencies among the single steps. The guideline emerges from various mapping studies and literature reviews conducted by the authors and provides recommendations for the general study design, data collection, and study selection procedures. Finally, we share our experiences and lessons learned in applying the different practices of the proposed guideline.

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Fußnoten
1
Note that finding the “right” research question is a challenge and highly depends on the actual study type. For instance, Kitchenham et al. (2015) mention (standard) research questions for systematic reviews usually addressing the evaluation of impact and/effectiveness of certain paradigms, while mapping studies usually address more high-level questions with the purpose of providing some sort of categorization. The questions presented in Table 1 are addressing more the latter aspect, as this covers information available from all sorts of studies. Nonetheless, to plan and implement a literature study efficiently, Staples and Niazi (2007) make clear that narrowly defined research questions are key. We therefore recommend to use a combination of generic research questions (e.g., Table 1 to “get a feeling” about the result set) and specific narrow research questions—even for mapping studies.
 
2
Note that the construction of search strings also depends on the planned search strategy (see Section 2.2), since search stings for automated database searches have a different “layout” than those used for a curiosity-driven or trail-and-error search, e.g., using Google Scholar. Regardless of the search strategy, finding the proper key words is crucial. The most straight-forward approach to develop appropriate search strings is either to do a trail-and-error search or to call in domain experts. Alternatively, a preliminary study can be conducted to “test” the field of interest.
 
3
As it is also criticized by Staples and Niazi (2007). In Kuhrmann et al. (2015), however, we accepted this challenge. It took us about a year just to clean the data and perform the selection procedures. We do not recommend this for replication.
 
5
Note: Apart from serving the backup search, meta-search engines can also be a useful instrument in studies that also include (continuous) updates, e.g., to monitor the development of a field over time (Kuhrmann et al. 2016).
 
6
Note: Some of the tools have limitations regarding the amount of text they can process. Furthermore, the tools offer different features, such as thresholds, visualization and export mechanisms. Those points need to be evaluated prior to usage.
 
8
Please note that a reviewer can be an internal reviewer (e.g., a co-author) as well as an external researcher or expert not involved at all in the design in case of unknown domains.
 
9
So far, we did not yet apply this method to a complete study, but partially applied it during sample-based result set testing and evaluation (cf. Section 3). As this approach is quite complex compared to the majority vote, it requires sufficient tool support.
 
10
Please note that inter-rater reliability calculations also depend on the scales applied, e.g., weighted κ values when using ordinal data (cf. Kitchenham et al. 2015; Wohlin et al. 2012).
 
11
This approach needs to be considered with care, as for instance newer publications may have a high-quality contribution, but don’t have a high citation count (e.g., compared to a 10-year old publication). Therefore, citation networks only deliver initial indication and trends shouldn’t be taken for granted.
 
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Metadaten
Titel
On the pragmatic design of literature studies in software engineering: an experience-based guideline
verfasst von
Marco Kuhrmann
Daniel Méndez Fernández
Maya Daneva
Publikationsdatum
06.01.2017
Verlag
Springer US
Erschienen in
Empirical Software Engineering / Ausgabe 6/2017
Print ISSN: 1382-3256
Elektronische ISSN: 1573-7616
DOI
https://doi.org/10.1007/s10664-016-9492-y

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