Inferring hierarchical motifs from execution traces

Alimadadi, Saba; Mesbah, Ali; Pattabiraman, Karthik

doi:10.1145/3180155.3180216

Cited by 25 publications

(15 citation statements)

References 67 publications

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“…We differ in addressing by similar techniques the traces of general-purpose programs. Analysis of execution traces is a very active research area in software engineering (e.g., recent works are [AMP18,GRS17]) and process engineering (e.g., [BvdA12]). We believe that a model-driven management of traces can enhance the interoperability of such solutions.…”

Section: Class Methods Statement Test_ma_0mentioning

confidence: 99%

Source-Code Level Regression Test Selection: the Model-Driven Way.

Fosse¹,

Mottu²,

Tisi³

et al. 2019

JOT

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In order to ensure that existing functionalities have not been impacted by recent program changes, test cases are regularly executed during regression testing (RT) phases. The RT time becomes problematic as the number of test cases is growing. Regression test selection (RTS) aims at running only the test cases that have been impacted by recent changes. RTS reduces the duration of regression testing and hence its cost. In this paper, we present a model-driven approach for RTS. Execution traces are gathered at runtime, and injected in a static source-code model. We use this resulting model to identify and select all the test cases that have been impacted by changes between two revisions of the program. Our MDE approach allows modularity in the granularity of changes considered. In addition, it offers better reusability than existing RTS techniques: the trace model is persistent and standardised. Furthermore, it enables more interoperability with other model-driven tools, enabling further analysis at different levels of abstraction (e.g., energy consumption). We evaluate our approach by applying it to four well-known opensource projects. Results show that our MDE proposal can effectively reduce the execution time of RT, by up to 32 % in our case studies. The overhead induced by the model building makes our approach slower than dedicated RTS tools, but the reuse of trace models for further analysis compensates for the time.

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Section: Class Methods Statement Test_ma_0mentioning

confidence: 99%

Source-Code Level Regression Test Selection: the Model-Driven Way.

Fosse¹,

Mottu²,

Tisi³

et al. 2019

JOT

View full text Add to dashboard Cite

show abstract

“…In recent years, increasing awareness of the need to manage non-determinism in parallel applications [2] has given rise to several software solutions. The two solutions that most closely resemble our framework are PopMine [6] and SABALAN [5]. PopMine [6] is a tool that analyzes traces of message orders in MPI applications, also represented by an event graph-like structure to determine a minimal DAG that triggers a given bug.…”

Section: Related Workmentioning

confidence: 99%

“…SABALAN [5] is a tool that analyzes trace data, albeit not from MPI applications, and finds hierarchies of motifs that can potentially be linked in bug manifestation. In contrast with SABALAN, our framework compares communication patterns across multiple executions, rather than searching for possibly non-deterministic execution motifs within a single run.…”

Section: Related Workmentioning

confidence: 99%

Identifying Degree and Sources of Non-Determinism in MPI Applications Via Graph Kernels

Chapp

Tan

Bhowmick

et al. 2021

IEEE Trans. Parallel Distrib. Syst.

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As the scientific community prepares to deploy an increasingly complex and diverse set of applications on exascale platforms, the need to assess reproducibility of simulations and identify the root causes of reproducibility failures increases correspondingly. One of the greatest challenges facing reproducibility issues at exascale is the inherent non-determinism at the level of inter-process communication. The use of non-deterministic communication constructs are necessary to boost performance, but communication non-determinism can also hamper software correctness and result reproducibility.To address this challenge, we propose a software framework for identifying the percentage and sources of communication nondeterminism. We model parallel executions as directed graphs and leverage graph kernels to characterize run-to-run variations in interprocess communication. We demonstrate the effectiveness of graph kernel similarity as a proxy for non-determinism, by showing that these kernels can quantify the type and degree of non-determinism present in communication patterns. To demonstrate our framework's ability to link and quantify runtime non-determinism to root sources, we demonstrate results for an adaptive mesh refinement application, where our framework automatically quantifies the impact of function calls on nondeterminism, and a Monte Carlo application, where our framework automatically quantifies the impact of parameter configurations on nondeterminism.

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“…There are numerous dynamic analysis techniques based on collecting and analyzing JavaScript traces selectively [Amalfitano et al 2014;Hibschman and Zhang 2015]. Some assist with the process of comprehension and debugging by utilizing information visualization techniques and providing higher-level models of execution [Alimadadi et al 2018[Alimadadi et al , 2016bMaras et al 2013;Zaidman et al 2013]. Andreasen et al [2017] present a survey of dynamic analysis techniques for JavaScript.…”

Section: Related Workmentioning

confidence: 99%

Finding broken promises in asynchronous JavaScript programs

Alimadadi

Zhong

Madsen

et al. 2018

Proc. ACM Program. Lang.

Self Cite

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Recently, promises were added to ECMAScript 6, the JavaScript standard, in order to provide better support for the asynchrony that arises in user interfaces, network communication, and non-blocking I/O. Using promises, programmers can avoid common pitfalls of event-driven programming such as event races and the deeply nested counterintuitive control flow referred to as łcallback hellž. Unfortunately, promises have complex semantics and the intricate controlś and data-flow present in promise-based code hinders program comprehension and can easily lead to bugs. The promise graph was proposed as a graphical aid for understanding and debugging promise-based code. However, it did not cover all promise-related features in ECMAScript 6, and did not present or evaluate any technique for constructing the promise graphs. In this paper, we extend the notion of promise graphs to include all promise-related features in ECMAScript 6, including default reactions, exceptions, and the synchronization operations race and all. Furthermore, we report on the construction and evaluation of PromiseKeeper, which performs a dynamic analysis to create promise graphs and infer common promise anti-patterns. We evaluate PromiseKeeper by applying it to 12 open source promise-based Node.js applications. Our results suggest that the promise graphs constructed by PromiseKeeper can provide developers with valuable information about occurrences of common anti-patterns in their promise-based code, and that promise graphs can be constructed with acceptable run-time overhead. CCS Concepts: • Software and its engineering → Software testing and debugging; Concurrent programming structures;

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Inferring hierarchical motifs from execution traces

Cited by 25 publications

References 67 publications

Source-Code Level Regression Test Selection: the Model-Driven Way.

Source-Code Level Regression Test Selection: the Model-Driven Way.

Identifying Degree and Sources of Non-Determinism in MPI Applications Via Graph Kernels

Finding broken promises in asynchronous JavaScript programs

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