Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userQuality Attributes in Practice: Contemporary Data
57
0
0.0
(
0
)
Added by
Manuel Mazzara
Publication date
2016
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
It is well known that the software process in place impacts the quality of the resulting product. However, the specific way in which this effect occurs is still mostly unknown and reported through anecdotes. To gather a better understanding of such relationship, a very large survey has been conducted during the last year and has been completed by more than 100 software developers and engineers from 21 countries. We have used the percentage of satisfied customers estimated by the software developers and engineers as the main dependent variable. The results evidence some interesting patterns, like that quality attribute of which customers are more satisfied appears functionality, architectural styles may not have a significant influence on quality, agile methodologies might result in happier customers, larger companies and shorter projects seems to produce better products.
rate research
Read More
Context: Architecture Tactics (ATs) are architectural building blocks that provide general architectural solutions for addressing Quality Attributes (QAs) issues. Mining and analyzing QA-AT knowledge can help the software architecture community better understand architecture design. However, manually capturing and mining this knowledge is labor-intensive and difficult. Objective: Using Stack Overflow (SO) as our source, our main goals are to effectively mine such knowledge; and to have some sense of how developers use ATs with respect to QA concerns from related discussions. Methods: We applied a semi-automatic dictionary-based mining approach to extract the QA-AT posts in SO. With the mined QA-AT posts, we identified the relationships between ATs and QAs. Results: Our approach allow us to mine QA-AT knowledge effectively with an F-measure of 0.865 and Performance of 82.2%. Using this mining approach, we are able to discover architectural synonyms of QAs and ATs used by designers, from which we discover how developers apply ATs to address quality requirements. Conclusions: We make two contributions in this work: First, we demonstrated a semi-automatic approach to mine ATs and QAs from SO posts; Second, we identified little-known design relationships between QAs and ATs and grouped architectural design considerations to aid architects make architecture tactics design decisions.
The design of software systems inevitably enacts normative boundaries around the site of intervention. These boundaries are, in part, a reflection of the values, ethics, power, and politics of the situation and the process of design itself. This paper argues that Requirements Engineering (RE) require more robust frameworks and techniques to navigate the values implicit in systems design work. To this end, we present the findings from a case of action research where we employed Critical Systems Heuristics (CSH), a framework from Critical Systems Thinking (CST) during requirements gathering for Homesound, a system to safeguard elderly people living alone while protecting their autonomy. We use categories from CSH to inform expert interviews and reflection, showing how CSH can be simply combined with RE techniques (such as the Volere template) to explore and reveal the value-judgements underlying requirements.
Context: The utility of prediction models in empirical software engineering (ESE) is heavily reliant on the quality of the data used in building those models. Several data quality challenges such as noise, incompleteness, outliers and duplicate data points may be relevant in this regard. Objective: We investigate the reporting of three potentially influential elements of data quality in ESE studies: data collection, data pre-processing, and the identification of data quality issues. This enables us to establish how researchers view the topic of data quality and the mechanisms that are being used to address it. Greater awareness of data quality should inform both the sound conduct of ESE research and the robust practice of ESE data collection and processing. Method: We performed a targeted literature review of empirical software engineering studies covering the period January 2007 to September 2012. A total of 221 relevant studies met our inclusion criteria and were characterized in terms of their consideration and treatment of data quality. Results: We obtained useful insights as to how the ESE community considers these three elements of data quality. Only 23 of these 221 studies reported on all three elements of data quality considered in this paper. Conclusion: The reporting of data collection procedures is not documented consistently in ESE studies. It will be useful if data collection challenges are reported in order to improve our understanding of why there are problems with software engineering data sets and the models developed from them. More generally, data quality should be given far greater attention by the community. The improvement of data sets through enhanced data collection, pre-processing and quality assessment should lead to more reliable prediction models, thus improving the practice of software engineering.
Reliable empirical models such as those used in software effort estimation or defect prediction are inherently dependent on the data from which they are built. As demands for process and product improvement continue to grow, the quality of the data used in measurement and prediction systems warrants increasingly close scrutiny. In this paper we propose a taxonomy of data quality challenges in empirical software engineering, based on an extensive review of prior research. We consider current assessment techniques for each quality issue and proposed mechanisms to address these issues, where available. Our taxonomy classifies data quality issues into three broad areas: first, characteristics of data that mean they are not fit for modeling; second, data set characteristics that lead to concerns about the suitability of applying a given model to another data set; and third, factors that prevent or limit data accessibility and trust. We identify this latter area as of particular need in terms of further research.
Being able to access software in daily life is vital for everyone, and thus accessibility is a fundamental challenge for software development. However, given the number of accessibility issues reported by many users, e.g., in app reviews, it is not clear if accessibility is widely integrated into current software projects and how software projects address accessibility issues. In this paper, we report a study of the critical challenges and benefits of incorporating accessibility into software development and design. We applied a mixed qualitative and quantitative approach for gathering data from 15 interviews and 365 survey respondents from 26 countries across five continents to understand how practitioners perceive accessibility development and design in practice. We got 44 statements grouped into eight topics on accessibility from practitioners viewpoints and different software development stages. Our statistical analysis reveals substantial gaps between groups, e.g., practitioners have Direct v.s. Indirect accessibility relevant work experience when they reviewed the summarized statements. These gaps might hinder the quality of accessibility development and design, and we use our findings to establish a set of guidelines to help practitioners be aware of accessibility challenges and benefit factors. We also propose some remedies to resolve the gaps and to highlight key future research directions.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
