Apples, Oranges, and Software Engineering: Study Selection Challenges
for Secondary Research on Latent Variables
- URL: http://arxiv.org/abs/2402.08706v1
- Date: Tue, 13 Feb 2024 17:32:17 GMT
- Title: Apples, Oranges, and Software Engineering: Study Selection Challenges
for Secondary Research on Latent Variables
- Authors: Marvin Wyrich and Marvin Mu\~noz Bar\'on and Justus Bogner
- Abstract summary: The inability to measure abstract concepts directly poses a challenge for secondary studies in software engineering.
Standardized measurement instruments are rarely available, and even if they are, many researchers do not use them or do not even provide a definition for the studied concept.
SE researchers conducting secondary studies therefore have to decide a) which primary studies intended to measure the same construct, and b) how to compare and aggregate vastly different measurements for the same construct.
- Score: 8.612556181934291
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Software engineering (SE) is full of abstract concepts that are crucial for
both researchers and practitioners, such as programming experience, team
productivity, code comprehension, and system security. Secondary studies aimed
at summarizing research on the influences and consequences of such concepts
would therefore be of great value.
However, the inability to measure abstract concepts directly poses a
challenge for secondary studies: primary studies in SE can operationalize such
concepts in many ways. Standardized measurement instruments are rarely
available, and even if they are, many researchers do not use them or do not
even provide a definition for the studied concept. SE researchers conducting
secondary studies therefore have to decide a) which primary studies intended to
measure the same construct, and b) how to compare and aggregate vastly
different measurements for the same construct.
In this experience report, we discuss the challenge of study selection in SE
secondary research on latent variables. We report on two instances where we
found it particularly challenging to decide which primary studies should be
included for comparison and synthesis, so as not to end up comparing apples
with oranges. Our report aims to spark a conversation about developing
strategies to address this issue systematically and pave the way for more
efficient and rigorous secondary studies in software engineering.
Related papers
- Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.
Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
arXiv Detail & Related papers (2025-02-07T18:26:45Z) - Enriching Social Science Research via Survey Item Linking [11.902701975866595]
We model a task called Survey Item Linking (SIL) in two stages: mention detection and entity disambiguation.
To this end, we create a high-quality and richly annotated dataset consisting of 20,454 English and German sentences.
We demonstrate that the task is feasible, but observe that errors propagate from the first stage, leading to a lower overall task performance.
arXiv Detail & Related papers (2024-12-20T12:14:33Z) - Software analytics for software engineering: A tertiary review [2.7386485828693576]
We identify five secondary studies on the use of software analytics (SA) for software engineering (SE)
Despite the overlapping objectives and search time frames of these secondary studies, there is negligible overlap of primary studies between these secondary studies.
We conclude that an overview of the literature identified by these secondary studies would be useful in providing a more comprehensive overview of the topic.
arXiv Detail & Related papers (2024-10-08T08:28:03Z) - Teaching Software Metrology: The Science of Measurement for Software Engineering [10.23712090082156]
This chapter reviews key concepts in the science of measurement and applies them to software engineering research.
A series of exercises for applying important measurement concepts to the reader's research are included.
arXiv Detail & Related papers (2024-06-20T16:57:23Z) - Active Exploration via Experiment Design in Markov Chains [86.41407938210193]
A key challenge in science and engineering is to design experiments to learn about some unknown quantity of interest.
We propose an algorithm that efficiently selects policies whose measurement allocation converges to the optimal one.
In addition to our theoretical analysis, we showcase our framework on applications in ecological surveillance and pharmacology.
arXiv Detail & Related papers (2022-06-29T00:04:40Z) - Improving Students' Academic Performance with AI and Semantic
Technologies [0.0]
The aim of this study is to predict students' performance using marks from the previous semester, to model a course representation in a semantic way, and to identify the prerequisite between two similar courses.
The outcomes of this study can be summarized as: (i) a breakthrough result improves Manrique's work by 2.5% in terms of accuracy in dropout prediction; (ii) uncover the similarity between courses based on course description; (iii) identify the prerequisite over three compulsory courses of School of Computing at ANU.
arXiv Detail & Related papers (2022-05-02T06:11:24Z) - Wizard of Search Engine: Access to Information Through Conversations
with Search Engines [58.53420685514819]
We make efforts to facilitate research on CIS from three aspects.
We formulate a pipeline for CIS with six sub-tasks: intent detection (ID), keyphrase extraction (KE), action prediction (AP), query selection (QS), passage selection (PS) and response generation (RG)
We release a benchmark dataset, called wizard of search engine (WISE), which allows for comprehensive and in-depth research on all aspects of CIS.
arXiv Detail & Related papers (2021-05-18T06:35:36Z) - AR-LSAT: Investigating Analytical Reasoning of Text [57.1542673852013]
We study the challenge of analytical reasoning of text and introduce a new dataset consisting of questions from the Law School Admission Test from 1991 to 2016.
We analyze what knowledge understanding and reasoning abilities are required to do well on this task.
arXiv Detail & Related papers (2021-04-14T02:53:32Z) - Phase Transition Behavior in Knowledge Compilation [52.68422776053012]
We study the behaviour of size and compile-time behaviour for random k-CNF formulas in the context of knowledge compilation.
Our work is similar in spirit to the early work in CSP community on phase transition behavior in SAT/CSP.
arXiv Detail & Related papers (2020-07-20T18:36:27Z) - Secondary Studies in the Academic Context: A Systematic Mapping and
Survey [4.122293798697967]
The main goal of this study is to provide an overview on the use of secondary studies in an academic context.
We conducted an SM to identify the available and relevant studies on the use of secondary studies as a research methodology for conducting SE research projects.
Secondly, a survey was performed with 64 SE researchers to identify their perception related to the value of performing secondary studies to support their research projects.
arXiv Detail & Related papers (2020-07-10T20:01:26Z) - A Survey on Causal Inference [64.45536158710014]
Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy and economics.
Various causal effect estimation methods for observational data have sprung up.
arXiv Detail & Related papers (2020-02-05T21:35:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.