| 1. |
- Cito, Jurgen, et al.
(författare)
-
Interactive Production Performance Feedback in the IDE
- 2019
-
Ingår i: Proceedings - International Conference on Software Engineering. - 0270-5257. ; 2019-May, s. 971-981
-
Konferensbidrag (refereegranskat)abstract
- Performance problems are hard to track and debug, especially when detected in production and originating from development. Software developers try to reproduce the perfor- mance problem locally and debug it in the source code. However, production environments are too different to what profiling and testing can simulate locally in development environments. Software developers need to consult production monitoring tools to reason about and debug the issue. We propose an integrated approach that constructs an In-IDE performance model from monitoring data from production environments. When developers change source code, we perform incremental analysis to update our performance model to reflect the impact of these changes. This allows us to provide performance feedback to developers in near real time to enable them to prevent performance problems from reaching production. We present a tool, PerformanceHat, an Eclipse plugin that we evaluated in a controlled experiment with 20 professional software developers, in which they work on soft- ware maintenance tasks using our approach and a representative baseline (Kibana). We found that developers were significantly faster in (1) detecting the performance problem, and (2) finding the root-cause of the problem. We conclude that our approach helps detect, prevent and debug performance problems faster.
|
|
| 2. |
- Cito, Jürgen, et al.
(författare)
-
PerformanceHat - Augmenting Source Code with Runtime Performance Traces in the IDE
- 2018
-
Ingår i: Proceedings - International Conference on Software Engineering. - New York, NY, USA : ACM. - 0270-5257.
-
Konferensbidrag (refereegranskat)abstract
- Performance problems observed in production environments that have their origin in program code are immensely hard to localize and prevent. Data that can help solve such problems is usually found in external dashboards and is thus not integrated into the software development process. We propose an approach that augments source code with runtime traces to tightly integrate runtime performance traces into developer workflows. Our goal is to create operational awareness of performance problems in developers' code and contextualize this information to tasks they are currently working on. We implemented this approach as an Eclipse IDE plugin for Java applications that is available as an open source project on GitHub. A video of PerformanceHat in action is online: https://youtu.be/fTBBiylRhag.
|
|
| 3. |
- Newbury, Rhys, et al.
(författare)
-
Deep Learning Approaches to Grasp Synthesis: A Review
- 2023
-
Ingår i: IEEE Transactions on robotics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1552-3098 .- 1941-0468. ; 39:5, s. 3994-4015
-
Tidskriftsartikel (refereegranskat)abstract
- Grasping is the process of picking up an object by applying forces and torques at a set of contacts. Recent advances in deep learning methods have allowed rapid progress in robotic object grasping. In this systematic review, we surveyed the publications over the last decade, with a particular interest in grasping an object using all six degrees of freedom of the end-effector pose. Our review found four common methodologies for robotic grasping: sampling-based approaches, direct regression, reinforcement learning, and exemplar approaches In addition, we found two 'supporting methods' around grasping that use deep learning to support the grasping process, shape approximation, and affordances. We have distilled the publications found in this systematic review (85 papers) into ten key takeaways we consider crucial for future robotic grasping and manipulation research.
|
|
| 4. |
- Schermann, Gerald, et al.
(författare)
-
Continuous Experimentation - Challenges, Implementation Techniques, and Current Research
- 2018
-
Ingår i: IEEE Software. - 1937-4194 .- 0740-7459. ; 35:2, s. 26-31
-
Tidskriftsartikel (refereegranskat)abstract
- Continuous experimentation is an up-and-coming technique for requirements engineering and testing, particularly for web-based systems. On the basis of a practitioner survey, this article gives an overview of challenges, implementation techniques, and current research in the field. This article is part of a theme issue on release engineering.
|
|
| 5. |
- Schermann, Gerald, et al.
(författare)
-
We’re Doing It Live: A Multi-Method Empirical Study on Continuous Experimentation
- 2018
-
Ingår i: Information and Software Technology. - : Elsevier BV. - 0950-5849. ; 99, s. 41-57
-
Tidskriftsartikel (refereegranskat)abstract
- Context: Continuous experimentation guides development activities based on data collected on a subset of online users on a new experimental version of the software. It includes practices such as canary releases, gradual rollouts, dark launches, or A/B testing. Objective: Unfortunately, our knowledge of continuous experimentation is currently primarily based on well-known and outspoken industrial leaders. To assess the actual state of practice in continuous experimentation, we conducted a mixed-method empirical study. Method: In our empirical study consisting of four steps, we interviewed 31 developers or release engineers, and performed a survey that attracted 187 complete responses. We analyzed the resulting data using statistical analysis and open coding. Results: Our results lead to several conclusions: (1) from a software architecture perspective, continuous experimentation is especially enabled by architectures that foster independently deployable services, such as microservices-based architectures; (2) from a developer perspective, experiments require extensive monitoring and analytics to discover runtime problems, consequently leading to developer on call policies and influencing the role and skill sets required by developers; and (3) from a process perspective, many organizations conduct experiments based on intuition rather than clear guidelines and robust statistics. Conclusion: Our findings show that more principled and structured approaches for release decision making are needed, striving for highly automated, systematic, and data- and hypothesis-driven deployment and experimentation.
|
|
