1,240 research outputs found
Rationale Management Challenges in Requirements Engineering
Rationale and rationale management have been playing an increasingly prominent role in software system development mainly due to the knowledge demand during system evaluation, maintenance, and evolution, especially for large and complex systems. The rationale management for requirements engineering, as a commencing and critical phase in software development life cycle, is still under-exploited. In this paper, we first survey briefly the state-of-the-art on rationale employment and applications in requirements engineering. Secondly, we identify the challenges in integrating rationale management in requirements engineering activities in order to promote further investigations and define a research agenda on rationale management in requirements engineering.
Quality measures for ETL processes: from goals to implementation
Extraction transformation loading (ETL) processes play an increasingly important role for the support of modern business operations. These business processes are centred around artifacts with high variability and diverse lifecycles, which correspond to key business entities. The apparent complexity of these activities has been examined through the prism of business process management, mainly focusing on functional requirements and performance optimization. However, the quality dimension has not yet been thoroughly investigated, and there is a need for a more human-centric approach to bring them closer to business-users requirements. In this paper, we take a first step towards this direction by defining a sound model for ETL process quality characteristics and quantitative measures for each characteristic, based on existing literature. Our model shows dependencies among quality characteristics and can provide the basis for subsequent analysis using goal modeling techniques. We showcase the use of goal modeling for ETL process design through a use case, where we employ the use of a goal model that includes quantitative components (i.e., indicators) for evaluation and analysis of alternative design decisions.Peer ReviewedPostprint (author's final draft
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
Quick interaction between a human teacher and a learning machine presents
numerous benefits and challenges when working with web-scale data. The human
teacher guides the machine towards accomplishing the task of interest. The
learning machine leverages big data to find examples that maximize the training
value of its interaction with the teacher. When the teacher is restricted to
labeling examples selected by the machine, this problem is an instance of
active learning. When the teacher can provide additional information to the
machine (e.g., suggestions on what examples or predictive features should be
used) as the learning task progresses, then the problem becomes one of
interactive learning.
To accommodate the two-way communication channel needed for efficient
interactive learning, the teacher and the machine need an environment that
supports an interaction language. The machine can access, process, and
summarize more examples than the teacher can see in a lifetime. Based on the
machine's output, the teacher can revise the definition of the task or make it
more precise. Both the teacher and the machine continuously learn and benefit
from the interaction.
We have built a platform to (1) produce valuable and deployable models and
(2) support research on both the machine learning and user interface challenges
of the interactive learning problem. The platform relies on a dedicated,
low-latency, distributed, in-memory architecture that allows us to construct
web-scale learning machines with quick interaction speed. The purpose of this
paper is to describe this architecture and demonstrate how it supports our
research efforts. Preliminary results are presented as illustrations of the
architecture but are not the primary focus of the paper
Code Smells and Refactoring: A Tertiary Systematic Review of Challenges and Observations
In this paper, we present a tertiary systematic literature review of previous
surveys, secondary systematic literature reviews, and systematic mappings. We
identify the main observations (what we know) and challenges (what we do not
know) on code smells and refactoring. We show that code smells and refactoring
have a strong relationship with quality attributes, i.e., with
understandability, maintainability, testability, complexity, functionality, and
reusability. We argue that code smells and refactoring could be considered as
the two faces of a same coin. Besides, we identify how refactoring affects
quality attributes, more than code smells. We also discuss the implications of
this work for practitioners, researchers, and instructors. We identify 13 open
issues that could guide future research work. Thus, we want to highlight the
gap between code smells and refactoring in the current state of
software-engineering research. We wish that this work could help the
software-engineering research community in collaborating on future work on code
smells and refactoring
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