Using High-Rising Cities to Visualize Performance in Real-Time

For developers concerned with a performance drop or improvement in their software, a profiler allows a developer to quickly search and identify bottlenecks and leaks that consume much execution time. Non real-time profilers analyze the history of already executed stack traces, while a real-time profiler outputs the results concurrently with the execution of software, so users can know the results instantaneously. However, a real-time profiler risks providing overly large and complex outputs, which is difficult for developers to quickly analyze. In this paper, we visualize the performance data from a real-time profiler. We visualize program execution as a three-dimensional (3D) city, representing the structure of the program as artifacts in a city (i.e., classes and packages expressed as buildings and districts) and their program executions expressed as the fluctuating height of artifacts. Through two case studies and using a prototype of our proposed visualization, we demonstrate how our visualization can easily identify performance issues such as a memory leak and compare performance changes between versions of a program. A demonstration of the interactive features of our prototype is available at https://youtu.be/eleVo19Hp4k.Comment: 10 pages, VISSOFT 2017, Artifact: https://github.com/sefield/high-rising-city-artifac

[技術・研究報告] Eclipse上で動作する効率の良いオンラインおよびオフライン動的解析プラットフォーム

ソフトウェア工学のさまざまな分野において動的解析技術が用いられている．動的解析とは，プログラムの実行時の情報を収集し解析を行うプログラム解析技術で，解析対象となるプログラムの実行終了後に解析を行うオフライン解析と，実行の途中でそれまでに収集された情報の解析を行うオンライン解析に分類することができる．本論文では，オンラインおよびオフライン解析ツールの開発を支援するため，Eclipse上で動作する動的解析プラットフォームを開発したので報告する．具体的には，これまで，特にオンライン解析においてツール毎に個別に実装されてきた実行時情報の収集処理を共通化し，さらに収集された実行時情報を解析するための共通基盤をEclipse上で提供することによって開発の効率化を図る．本プラットフォームが提供する収集処理は，幅広い動的解析手法で利用できるよう詳細な実行時情報を効率よく収集できるという特長を持つ．またオンライン解析の共通基盤としては，解析アルゴリズムをオンライン化することによって生じるオーバーヘッドをできる限り抑えるよう設計上の工夫を行っている．本論文ではその設計を採用した場合と採用しない場合の解析速度の比較を行い，設計の妥当性について評価を行ったので報告する

Effective Segmentation of Large Execution Traces Using Probabilistic and Gaussian Mixture Models

Software maintenance is known to be a costly and time consuming activity. Software engineers need to spend a considerable amount of time in understanding the system before maintaining it. This is due to many reasons including the lack of good documentation and the shift of the original developers of the system to other projects or companies. Dynamic analysis techniques, more particularly trace analysis, are used to alleviate the program comprehension problem by offering software engineers a set of techniques that can help them understand the behavioural aspects of software systems. Execution traces however can be extremely large, which makes them cumbersome for effective analysis. There is a need to develop techniques to help software engineers understand the content of large traces despite their massive size. In this thesis, we present, SumTrace, a novel trace analysis technique. SumTrace takes a trace as input and automatically segments it into smaller and more manageable groups that reflect the execution phases of the traced scenario. The execution phases are summarized to help software engineers understand quickly different parts of the trace without having to analyze its entire content. SumTrace relies on a combination of probabilistic and Gaussian mixture models. We applied SumTrace to the segmentation of large traces, generated from two software systems. The results are very promising. SumTrace is also fast since it only requires only one pass through a trace

Feature-level Phase Detection for Execution Trace Using Object Cache

Visualizing collaborations of objects is important for developers understanding and debugging an object-oriented program. Many techniques and tools are proposed to visualize dynamic collaborations involved in an execution trace of a system, however, an execution trace may be too large to be transformed into a single diagram. In this paper, we propose a novel approach to efficiently detecting phases, or high-level behavioral units described in a use-case scenario. Our idea is based on the nature of object-oriented programming; a phase starts with preparing objects for the phase and ends with destroying temporary objects. Our technique uses a LRU cache for observing a working set of objects, and interprets a sharp rise in the frequency of the cache update as a phase transition. We have applied our approach to two industrial applications and found that our approach is promising to visualize a phase corresponding to a feature as a sequence diagram

Trace Abstraction Framework and Techniques

Understanding the behavioural aspects of software systems can help in a variety of software engineering tasks such as debugging, feature enhancement, performance analysis, and security. Software behaviour is typically represented in the form of execution traces. Traces, however, have historically been difficult to analyze due to the overwhelming size of typical traces. Trace analysis, more particularly trace abstraction and simplification, techniques have emerged to overcome the challenges of working with large traces. Existing traces analysis tools rely on some sort of visualization techniques to help software engineers make sense of trace content. Many of these techniques have been studied and found to be limited in many ways. In this thesis, we present a novel approach for trace analysis inspired by the way the human brain and perception systems operate. The idea is to mimic the psychological processes that have been developed over the years to explain how our perception system deals with huge volume of visual data. We show how similar mechanisms can be applied to the abstraction and simplification of large traces. As part of this framework, we present a novel trace analysis technique that automatically divides the content of a large trace, generated from execution of a target system, into meaningful segments that correspond to the system’s main execution phases such as initializing variables, performing a specific computation, etc. We also propose a trace sampling technique that not only reduces the size of a trace but also results in a sampled trace that is representative of the original trace by ensuring that the desired characteristics of an execution are distributed similarly in both the sampled and the original trace. Our approach is based on stratified sampling and uses the concept of execution phases as strata. Finally, we propose an approach to automatically identify the most relevant trace components of each execution phases. This approach also enables an efficient representation of the flow of phases by detecting redundant phases using a cosine similarity metric. The techniques presented in this thesis have been validated by applying to a variety of target systems. The obtained results demonstrate the effectiveness and usefulness of our methods