| 1. |
- Bayram, Firas, et al.
(författare)
-
DQSOps : Data Quality Scoring Operations Framework for Data-Driven Applications
- 2023
-
Ingår i: EASE '23: Proceedings of the 27<sup>th</sup> International Conference on Evaluation and Assessment in Software Engineering. - : Association for Computing Machinery (ACM). - 9798400700446 ; , s. 32-41
-
Konferensbidrag (refereegranskat)abstract
- Data quality assessment has become a prominent component in the successful execution of complex data-driven artificial intelligence (AI) software systems. In practice, real-world applications generate huge volumes of data at speeds. These data streams require analysis and preprocessing before being permanently stored or used in a learning task. Therefore, significant attention has been paid to the systematic management and construction of high-quality datasets. Nevertheless, managing voluminous and high-velocity data streams is usually performed manually (i.e. offline), making it an impractical strategy in production environments. To address this challenge, DataOps has emerged to achieve life-cycle automation of data processes using DevOps principles. However, determining the data quality based on a fitness scale constitutes a complex task within the framework of DataOps. This paper presents a novel Data Quality Scoring Operations (DQSOps) framework that yields a quality score for production data in DataOps workflows. The framework incorporates two scoring approaches, an ML prediction-based approach that predicts the data quality score and a standard-based approach that periodically produces the ground-truth scores based on assessing several data quality dimensions. We deploy the DQSOps framework in a real-world industrial use case. The results show that DQSOps achieves significant computational speedup rates compared to the conventional approach of data quality scoring while maintaining high prediction performance.
|
|
| 2. |
- Borg, Markus, et al.
(författare)
-
U Owns the Code That Changes and How Marginal Owners Resolve Issues Slower in Low-Quality Source Code
- 2023
-
Ingår i: ACM International Conference Proceeding Series. - 9798400700446 ; , s. 368-377
-
Konferensbidrag (refereegranskat)abstract
- [Context] Accurate time estimation is a critical aspect of predictable software engineering. Previous work shows that low source code quality increases the uncertainty in issue resolution times. [Objective] Our goal is to evaluate how developers' project experience and file ownership are related to issue resolution times. [Method] We mine 40 proprietary software repositories and conduct an observational study. Using CodeScene, we measure source code quality and active development time connected to Jira issues. [Results] Most source code changes are made by either a marginal or dominant code owner. Also, most changes to low-quality source code are made by developers with low levels of ownership. In low-quality source code, marginal owners need 45% more time for small changes, and 93% more time for large changes. [Conclusions] Collective code ownership is a popular target, but industry practice results in many dominant and marginal owners. Marginal owners are particularly hampered when working with low-quality source code, which leads to productivity losses. In codebases plagued by technical debt, newly onboarded developers will require more time to complete tasks.
|
|
| 3. |
- Chen, Xingru, et al.
(författare)
-
Using InnerSource for Improving Internal Reuse : An Industrial Case Study
- 2023
-
Ingår i: ACM International Conference Proceeding Series. - : Association for Computing Machinery (ACM). - 9798400700446 ; , s. 348-357
-
Konferensbidrag (refereegranskat)abstract
- Background: InnerSource consists of the use of open source development techniques within the corporation. It helps improve software reuse through increased transparency and inter-team collaboration. Companies need to understand their context and specific needs before deciding to adopt any specific InnerSource practices since they cannot apply all InnerSource practices at once. Aim: This study aims to support the case company in assessing its readiness for adopting InnerSource practices to improve its internal reuse, identify and prioritize the improvement areas, and identify suitable solutions. Method: We performed a case study using a questionnaire and a workshop to check the current and desired status of adopting InnerSource practices and collect potential solutions. Results: The study participants identified that the company needs to prioritize the improvements related to the discoverability, communication channels, and ownership of the reusable assets. In addition, they identified certain InnerSource practices as solutions for the prioritized improvement areas, such as better structured repositories for storing and searching the reusable assets and standardized documentation of the reusable assets. Conclusion: The questionnaire instrument aids the case company in identifying the improvement areas related to InnerSource and reuse practices. InnerSource practices could improve the development and maintenance of reusable assets. Keywords: InnerSource, software reuse, readiness © 2023 Owner/Author.
|
|
| 4. |
- Couderc, Noric, et al.
(författare)
-
Classification-based Static Collection Selection for Java: Effectiveness and Adaptability
- 2023
-
Ingår i: EASE '23: Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering. - 9798400700446 ; , s. 111-120
-
Konferensbidrag (refereegranskat)abstract
- Carefully selecting the right collection datastructure can significantly improve the performance of a Java program. Unfortunately, the performance impact of a certain collection selection can be hard to estimate.To assist developers there are tools that recommend collections to use based on static and/or dynamic information about a program. The majority of existing collection selection tools for Java (e.g., CoCo, CollectionSwitch) pick their selections dynamically, which means that they must trade off sophistication in their selection algorithm against its run time overhead.For static collection selection, the Brainy tool has demonstrated that complex, machine-dependent models can produce substantial performance improvements, albeit only for C++ so far.In this paper, we port Brainy from C++ to Java, and evaluate its effectiveness for 5 benchmarks from the DaCapo benchmark suite. We compare it against the original program, but also to a variant of a brute-force approach to collection selection, which serves as our ground truth for optimal performance. Our results show that in four benchmarks out of five, our ground truth and the original program are similar. In one case, the ground truth shows an optimization yielding 15% speedup was available, but our port did not find this substantial optimization. We find that the port is more efficient but less effective than the ground truth, can easily adapt to new hardware architectures, and incorporate new datastructures with at most a few hours of human effort. We detail challenges that we encountered porting the Brainy approach to Java, and list a number of insights and directions for future research.
|
|
| 5. |
- Sarika, Pawan Kumar, et al.
(författare)
-
Automating Microservices Test Failure Analysis using Kubernetes Cluster Logs
- 2023
-
Ingår i: ACM International Conference Proceeding Series. - : Association for Computing Machinery (ACM). - 9798400700446 ; , s. 192-195
-
Konferensbidrag (refereegranskat)abstract
- Kubernetes is a free, open-source container orchestration system for deploying and managing Docker containers that host microservices. Kubernetes cluster logs help in determining the reason for the failure. However, as systems become more complex, identifying failure reasons manually becomes more difficult and time-consuming. This study aims to identify effective and efficient classification algorithms to automatically determine the failure reason. We compare five classification algorithms, Support Vector Machines, K-Nearest Neighbors, Random Forest, Gradient Boosting Classifier, and Multilayer Perceptron. Our results indicate that Random Forest produces good accuracy while requiring fewer computational resources than other algorithms. © 2023 Owner/Author.
|
|
