103,750 research outputs found
Guidelines for the Search Strategy to Update Systematic Literature Reviews in Software Engineering
Context: Systematic Literature Reviews (SLRs) have been adopted within
Software Engineering (SE) for more than a decade to provide meaningful
summaries of evidence on several topics. Many of these SLRs are now potentially
not fully up-to-date, and there are no standard proposals on how to update SLRs
in SE. Objective: The objective of this paper is to propose guidelines on how
to best search for evidence when updating SLRs in SE, and to evaluate these
guidelines using an SLR that was not employed during the formulation of the
guidelines. Method: To propose our guidelines, we compare and discuss outcomes
from applying different search strategies to identify primary studies in a
published SLR, an SLR update, and two replications in the area of effort
estimation. These guidelines are then evaluated using an SLR in the area of
software ecosystems, its update and a replication. Results: The use of a single
iteration forward snowballing with Google Scholar, and employing as a seed set
the original SLR and its primary studies is the most cost-effective way to
search for new evidence when updating SLRs. Furthermore, the importance of
having more than one researcher involved in the selection of papers when
applying the inclusion and exclusion criteria is highlighted through the
results. Conclusions: Our proposed guidelines formulated based upon an effort
estimation SLR, its update and two replications, were supported when using an
SLR in the area of software ecosystems, its update and a replication.
Therefore, we put forward that our guidelines ought to be adopted for updating
SLRs in SE.Comment: Author version of manuscript accepted for publication at the
Information and Software Technology Journa
Doctor of Philosophy
dissertationMedical knowledge learned in medical school can become quickly outdated given the tremendous growth of the biomedical literature. It is the responsibility of medical practitioners to continuously update their knowledge with recent, best available clinical evidence to make informed decisions about patient care. However, clinicians often have little time to spend on reading the primary literature even within their narrow specialty. As a result, they often rely on systematic evidence reviews developed by medical experts to fulfill their information needs. At the present, systematic reviews of clinical research are manually created and updated, which is expensive, slow, and unable to keep up with the rapidly growing pace of medical literature. This dissertation research aims to enhance the traditional systematic review development process using computer-aided solutions. The first study investigates query expansion and scientific quality ranking approaches to enhance literature search on clinical guideline topics. The study showed that unsupervised methods can improve retrieval performance of a popular biomedical search engine (PubMed). The proposed methods improve the comprehensiveness of literature search and increase the ratio of finding relevant studies with reduced screening effort. The second and third studies aim to enhance the traditional manual data extraction process. The second study developed a framework to extract and classify texts from PDF reports. This study demonstrated that a rule-based multipass sieve approach is more effective than a machine-learning approach in categorizing document-level structures and iv that classifying and filtering publication metadata and semistructured texts enhances the performance of an information extraction system. The proposed method could serve as a document processing step in any text mining research on PDF documents. The third study proposed a solution for the computer-aided data extraction by recommending relevant sentences and key phrases extracted from publication reports. This study demonstrated that using a machine-learning classifier to prioritize sentences for specific data elements performs equally or better than an abstract screening approach, and might save time and reduce errors in the full-text screening process. In summary, this dissertation showed that there are promising opportunities for technology enhancement to assist in the development of systematic reviews. In this modern age when computing resources are getting cheaper and more powerful, the failure to apply computer technologies to assist and optimize the manual processes is a lost opportunity to improve the timeliness of systematic reviews. This research provides methodologies and tests hypotheses, which can serve as the basis for further large-scale software engineering projects aimed at fully realizing the prospect of computer-aided systematic reviews
Application of Developers' and Users' Dependent Factors in App Store Optimization
This paper presents an application of developers' and users' dependent
factors in the app store optimization. The application is based on two main
fields: developers' dependent factors and users' dependent factors. Developers'
dependent factors are identified as: developer name, app name, subtitle, genre,
short description, long description, content rating, system requirements, page
url, last update, what's new and price. Users' dependent factors are identified
as: download volume, average rating, rating volume and reviews. The proposed
application in its final form is modelled after mining sample data from two
leading app stores: Google Play and Apple App Store. Results from analyzing
collected data show that developer dependent elements can be better optimized.
Names and descriptions of mobile apps are not fully utilized. In Google Play
there is one significant correlation between download volume and number of
reviews, whereas in App Store there is no significant correlation between
factors
Research Findings on Empirical Evaluation of Requirements Specifications Approaches
Numerous software requirements specification (SRS) approaches have been proposed in software engineering. However, there has been little empirical evaluation of the use of these approaches in specific contexts. This paper describes the results of a mapping study, a key instrument of the evidence-based paradigm, in an effort to understand what aspects of SRS are evaluated, in which context, and by using which research method. On the basis of 46 identified and categorized primary studies, we found that understandability is the most commonly evaluated aspect of SRS, experiments are the most commonly used research method, and the academic environment is where most empirical evaluation takes place
Safety-Critical Systems and Agile Development: A Mapping Study
In the last decades, agile methods had a huge impact on how software is
developed. In many cases, this has led to significant benefits, such as quality
and speed of software deliveries to customers. However, safety-critical systems
have widely been dismissed from benefiting from agile methods. Products that
include safety critical aspects are therefore faced with a situation in which
the development of safety-critical parts can significantly limit the potential
speed-up through agile methods, for the full product, but also in the
non-safety critical parts. For such products, the ability to develop
safety-critical software in an agile way will generate a competitive advantage.
In order to enable future research in this important area, we present in this
paper a mapping of the current state of practice based on {a mixed method
approach}. Starting from a workshop with experts from six large Swedish product
development companies we develop a lens for our analysis. We then present a
systematic mapping study on safety-critical systems and agile development
through this lens in order to map potential benefits, challenges, and solution
candidates for guiding future research.Comment: Accepted at Euromicro Conf. on Software Engineering and Advanced
Applications 2018, Prague, Czech Republi
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