Supporting the Selection of Constraints for Requirements Monitoring from Automatically Mined Constraint Candidates

Krismayer, Thomas; Kronberger, Peter; Rabiser, Rick; Grünbacher, Paul

doi:10.1007/978-3-030-15538-4_15

Cited by 4 publications

(7 citation statements)

References 20 publications

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“…He also gave two examples for constraints he found surprising at first but understood after a second look. The domain expert also rated the grouping of constraints [6] our approach provides as meaningful.…”

Section: Results Of the Qualitative Evaluation (Rq1)mentioning

confidence: 99%

“…Finally, the domain expert answered open questions to provide additional feedback and comments. Specifically, we asked if he was missing important constraints, whether the mined constraints triggered any ideas for additional constraints, how surprising he regarded the found constraints, and how well the automatic constraint grouping [6] works.…”

Section: Research Approachmentioning

confidence: 99%

“…Following this direction, we describe the application of a constraint mining approach [5], [6] developed in our earlier research on software monitoring [7] to analyze software development processes. We first extract data from an issue-tracking tool and then reveal process definitions in the form of constraints.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, our approach can extract different types of constraints on event occurrence, timing, and data (as well as combinations of these). The approach presents candidate constraints to users in a domain-specific language [8] and offers a range of filtering and ranking strategies [6] to select the constraints to be used for monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Using Constraint Mining to Analyze Software Development Processes

Krismayer

Mayr-Dorn²,

Tuder

et al. 2019

2019 IEEE/ACM International Conference on Software and System Processes (ICSSP)

Self Cite

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Most software development organizations nowadays use issue-tracking tools to manage software processes throughout the life-cycle. Still, understanding development processes, keeping track of process execution, and reacting to deviations in projects remains challenging. In particular, the actual process usually differs from the process perceived by developers, making it hard to define the processes developers are expected to carry out. This is further challenged by frequently changing processes and process variations in different projects and teams. In this paper we describe an empirical study in which we applied a constraint mining approach from the field of software monitoring to automatically extract process definitions in the form of constraints. Specifically, we applied the approach to datasets extracted from four real-world projects (using the Jira issue-tracking tool) in a company developing a recreational activities platform. The mined constraints describe the boundaries of the actual processes and thus help to understand process behavior. Constraints can be frequently re-mined to understand process evolution. The mined constraints can also be used to monitor future processes to detect problems in the development process early on. We involved a domain expert to evaluate the usefulness of our results and investigated to what extent the mined constraints reflect the official development process of the company. We also report mining results for different issue types, across projects, and over different time windows.

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Section: Results Of the Qualitative Evaluation (Rq1)mentioning

confidence: 99%

Section: Research Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using Constraint Mining to Analyze Software Development Processes

Krismayer

Mayr-Dorn²,

Tuder

et al. 2019

2019 IEEE/ACM International Conference on Software and System Processes (ICSSP)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our mining technique detects and analyzes recurring event patterns to extract constraints on event occurrence, timing, and event data (as well as combinations of these types). It presents the identified candidate constraints to users in a domain-specific language (DSL) [12] and offers a range of filtering and ranking strategies [7]. The approach presented in this paper uses constraint mining to re-mine constraints after changes to the system and then compares the constraint sets.…”

Section: Introductionmentioning

confidence: 99%

Comparing Constraints Mined From Execution Logs to Understand Software Evolution

Krismayer¹,

Vierhauser²,

Rabiser³

et al. 2019

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Complex software systems evolve frequently, e.g., when introducing new features or fixing bugs during maintenance. However, understanding the impact of such changes on system behavior is often difficult. Many approaches have thus been proposed that analyze systems before and after changes, e.g., by comparing source code, model-based representations, or system execution logs. In this paper, we propose an approach for comparing run-time constraints, synthesized by a constraint mining algorithm, based on execution logs recorded before and after changes. Specifically, automatically mined constraints define the expected timing and order of recurring events and the values of data elements attached to events. Our approach presents the differences of the mined constraints to users, thereby providing a higher-level view on software evolution and supporting the analysis of the impact of changes on system behavior. We present a motivating example and a preliminary evaluation based on a cyber-physical system controlling unmanned aerial vehicles. The results of our preliminary evaluation show that our approach can help to analyze changed behavior and thus contributes to understanding software evolution.

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