Home > Events > Oral Candidacy- Mona Rahimi

Oral Candidacy- Mona Rahimi

Start: 10/28/2016 at 1:00PM
End: 10/28/2016 at 3:30PM
Location: 258 Fitzpatrick Hall
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Oral Candidacy

Mona Rahimi

October 28, 2016

1:00 pm

258 Fitzpatrick Hall

Adviser:  Dr. Jane Cleland-Huang

Committee Members:

Dr. Nitesh Chawla          Dr. Collin McMillan          Dr. Robyn Lutz




Software evolution relates to the activity and phenomenon of software change [74]. Constant changes are inevitable in large software systems and especially in safety-critical systems for which failure or malfunction may result in death or serious injury to humans, damage to property, and/or environmental harm. Therefore, such systems must undergo a certification process before their release. The certification process commonly happens in discrete and costly steps and, once certified, the system is commonly closed to changes and adaption to avoid the need for re-certification. This effect is referred to as the "Big Freeze" [15]. In this dissertation, I summarize my contribution to enhance evolution in software intensive and particularly safety-critical systems. Enhanced evolution of such systems can also introduce the need for a continual evaluation of their safety during the maintenance phase of their development life cycle. First, I started my research with discovering patterns of change within software artifacts and also patterns of co-evolution between pairs of artifact types, particularly requirement and source code, across multiple versions of a software system. Second, I exploit these identified patterns to implement a Trace Link Evolver (TLE) which detects these change scenarios which occurred between subsequent versions of a system and then uses a corresponding set of link evolution heuristics and automatically evolves trace links between requirements and source code as changes are introduced to the system. The experimental results showed TLE achieves significantly higher accuracy than existing approaches. Third, I am currently developing a prototype visualization tool which illustrates the evolution of trace links, and their associated classes and features, in the form of a graph, by leveraging the links generated by TLE. TLE visualization provides developers with information they need to understand the evolution history, rationales, and underlying functionality of classes in the source code. Fourth, I leverage existing traceability information required by many certifying bodies for safety-critical systems by using information retrieval techniques to identify problematic change patterns in safety artifacts across versions of a system. Since historical data has shown that problems with the correctness and completeness of environmental assumptions contribute to many accidents in safety-critical systems therefore, I focused on changes to these particular sets of artifacts. In this approach named Assumption Diagnostics and Rationale Process (ADRP), I exploit the existing trace links to reason about the likelihood that assumptions are missing or incorrectly retained in the new software product. ADRP also provides useful information to assess the validity of environmental assumptions and finally recommends mitigation steps if a problem with assumptions is confirmed. Experimental results from evaluation of ADRP show that it consistently diagnosed problematic assumptions in three product lines. Finally, to address the problem of finding publicly available safety-critical projects with sufficiently complete set of artifacts and across multiple versions, I contributed in conducting five safety-critical projects as part of a six-month graduate Software Engineering capstone course at DePaul University. Approximately 20 additional hours per project were also invested to prepare and polish data sets for use. Also as my current plan, I am tending to first preprocess the artifact sets in three of these projects and then use existing information retrieval algorithms to establish a set of trace links between existing software and safety artifacts. This work can be a baseline for assessing the accuracy of existing information retrieval techniques in constructing trace links for safety artifacts

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