An official website of the United States government
In a software system’s development lifecycle, engineers make numerous design decisions that subsequently cause architectural change in the system. Previous studies have shown that, more often than not, these architectural changes are unintentional by-products of continual software maintenance tasks. The result of inadvertent architectural changes is accumulation of technical debt and deterioration of software quality. Despite their important implications, there is a relative shortage of techniques, tools, and empirical studies pertaining to architectural design decisions. In this paper, we take a step toward addressing that scarcity by using the information in the issue and code repositories of open-source software systems to investigate the cause and frequency of such architectural design decisions. Furthermore, building on these results, we develop a predictive model that is able to identify the architectural significance of newly submitted issues, thereby helping engineers to prevent the adverse effects of architectural decay. The results of this study are based on the analysis of 21,062 issues affecting 301 versions of 5 large open-source systems for which the code changes and issues were publicly accessible.
more »
« less
A Longitudinal Study of Identifying and Paying Down Architecture Debt
Architecture debt is a form of technical debt that derives from the gap between the intended and the actual architecture design. In this study we measured architecture debt in two ways: 1) in terms of system-wide coupling measures, and 2) in terms of the number and severity of architecture flaws. In recent research it was shown that the amount of architecture debt has a huge impact on software maintainability and evolution. Consequently, reducing debt is expected to make software less costly and more amenable to change. This paper reports on a longitudinal study of a healthcare communications product created by BrightSquid Secure Communications Corp. This young company is facing the typical trade-off problem of desiring responsiveness to change requests, but wanting to avoid the ever-increasing effort that the accumulation of quick-and- dirty changes eventually incurs. In the first stage of the study, we analyzed the status of the “before” system, which showed the impacts of change requests. This initial study motivated a more in-depth analysis of architecture debt. The results of this debt analysis were used in the second stage of the work to motivate a comprehensive refactoring of the software system. The third stage was a follow-on architecture debt analysis which quantified the improvements realized. Using this quantitative evidence, augmented by qualitative evidence gathered from in- depth interviews with BrightSquid’s architects, we present lessons learned about the costs and benefits of paying down architecture debt in practice.
more »
« less
- PAR ID:
- 10116992
- Date Published:
- Journal Name:
- 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice
- Page Range / eLocation ID:
- 171 to 180
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Dependencies among software entities are the basis for many software analytic research and architecture analysis tools. Dynamically typed languages, such as Python, JavaScript and Ruby, tolerate the lack of explicit type references, making certain syntactic dependencies indiscernible in source code. We call these possible dependencies, in contrast with the explicit dependencies that are directly referenced in source code. Type inference techniques have been widely studied and applied, but existing architecture analytic research and tools have not taken possible dependencies into consideration. The fundamental question is, to what extent will these missing possible dependencies impact the architecture analysis? To answer this question , we conducted an empirical study with 105 Python projects, using type inference techniques to manifest possible dependencies. Our study revealed that the architectural impact of possible dependencies is substantial-higher than that of explicit dependencies: (1) file-level possible dependencies account for at least 27.93% of all file-level dependencies, and create different dependency structures than that of explicit dependencies only, with an average difference of 30.71%; (2) adding possible dependencies significantly improves the precision (0.52%∼14.18%), recall(31.73%∼39.12%), and F1 scores (22.13%∼32.09%) of capturing co-change relations; (3) on average, a file involved in possible dependencies influences 28% more files and 42% more dependencies within architectural sub-spaces than a file involved in just explicit dependencies; (4) on average, a file involved in possible dependencies consumes 32% more maintenance effort. Consequently, maintainability scores reported by existing tools make a system written in these dynamic languages appear to be better modularized than it actually is. This evidence strongly * with the Ministry suggests that possible dependencies have a more significant impact than explicit dependencies on architecture quality, that architecture analysis and tools should assess and even emphasize the architectural impact of possible dependencies due to dynamic typing.more » « less
-
Poor design choices, bad coding practices, or the need to produce software quickly can stand behind technical debt. Unfortunately, manually identifying and managing technical debt gets more difficult as the software matures. Recent research offers various techniques to automate the process of detecting and managing technical debt to address these challenges. This manuscript presents a mapping study of the many aspects of technical debt that have been discovered in this field of study. This includes looking at the various forms of technical debt, as well as detection methods, the financial implications, and mitigation strategies. The findings and outcomes of this study are applicable to a wide range of software development life-cycle decisions.more » « less
-
Architecture degradation has a strong negative impact on software quality and can result in significant losses. Severe software degradation does not happen overnight. Software evolves continuously, through numerous issues, fixing bugs and adding new features, and architecture flaws emerge quietly and largely unnoticed until they grow in scope and significance when the system becomes difficult to maintain. Developers are largely unaware of these flaws or the accumulating debt as they are focused on their immediate tasks of address individual issues. As a consequence, the cumulative impacts of their activities, as they affect the architecture, go unnoticed. To detect these problems early and prevent them from accumulating into severe ones we propose to monitor software evolution by tracking the interactions among files revised to address issues. In particular, we propose and show how we can automatically detect active hotspots, to reveal architecture problems. We have studied hundreds of hotspots along the evolution timelines of 21 open source projects and showed that there exist just a few dominating active hotspots per project at any given time. Moreover, these dominating active hotspots persist over long time periods, and thus deserve special attention. Compared with state-of-the-art design and code smell detection tools we report that, using active hotspots, it is possible to detect signs of software degradation both earlier and more precisely.more » « less
-
Software design debt aims to elucidate the rectification attempts of the present design flaws and studies the influence of those to the cost and time of the software. Design smells are a key cause of incurring design debt. Although the impact of design smells on design debt have been predominantly considered in current literature, how design smells are caused due to not following software engineering best practices require more exploration. This research provides a tool which is used for design smell detection in Java software by analyzing large volume of source codes. More specifically, 409,539 Lines of Code (LoC) and 17,760 class files of open source Java software are analyzed here. Obtained results show desirable precision values ranging from 81.01% to 93.43%. Based on the output of the tool, a study is conducted to relate the cause of the detected design smells to two software engineering challenges namely "irregular team meetings" and "scope creep". As a result, the gained information will provide insight to the software engineers to take necessary steps of design remediation actions.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICSE-SEIP.2019.00026
