SATISFACTION ASSESSMENT OF TEXTUAL SOFTWARE ENGINEERING ARTIFACTS

A large number of software projects exist and will continue to be developed that have textual requirements and textual design elements where the design elements should fully satisfy the requirements. Current techniques to assess the satisfaction of requirements by corresponding design elements are largely manual processes that lack formal criteria and standard practices. Software projects that require satisfaction assessment are often very large systems containing several hundred requirements and design elements. Often these projects are within a high assurance project domain, where human lives and millions of dollars of funding are at stake. Manual satisfaction assessment is expensive in terms of hours of human effort and project budget. Automated techniques are not currently applied to satisfaction assessment. This dissertation addresses the problem of automated satisfaction assessment for English, textual documents and the generation of candidate satisfaction assessments that can then be verified by a human analyst with far less effort and time expenditure than is required to produce a manual satisfaction assessment. Validation results to date show that automated satisfaction methods produce candidate satisfaction assessments sufficient to greatly reduce the effort required to assess the satisfaction of textual requirements by textual design elements

Technique Integration for Requirements Assessment

In determining whether to permit a safety-critical software system to be certified and in performing independent verification and validation (IV&V) of safety- or mission-critical systems, the requirements traceability matrix (RTM) delivered by the developer must be assessed for accuracy. The current state of the practice is to perform this work manually, or with the help of general-purpose tools such as word processors and spreadsheets Such work is error-prone and person-power intensive. In this paper, we extend our prior work in application of Information Retrieval (IR) methods for candidate link generation to the problem of RTM accuracy assessment. We build voting committees from five IR methods, and use a variety of voting schemes to accept or reject links from given candidate RTMs. We report on the results of two experiments. In the first experiment, we used 25 candidate RTMs built by human analysts for a small tracing task involving a portion of a NASA scientific instrument specification. In the second experiment, we randomly seeded faults in the RTM for the entire specification. Results of the experiments are presented

Assessing Traceability of Software Engineering Artifacts

The generation of traceability links or traceability matrices is vital to many software engineering activities. It is also person-power intensive, time-consuming, error-prone, and lacks tool support. The activities that require traceability information include, but are not limited to, risk analysis, impact analysis, criticality assessment, test coverage analysis, and verification and validation of software systems. Information Retrieval (IR) techniques have been shown to assist with the automated generation of traceability links by reducing the time it takes to generate the traceability mapping. Researchers have applied techniques such as Latent Semantic Indexing (LSI), vector space retrieval, and probabilistic IR and have enjoyed some success. This paper concentrates on examining issues not previously widely studied in the context of traceability: the importance of the vocabulary base used for tracing and the evaluation and assessment of traceability mappings and methods using secondary measures. We examine these areas and perform empirical studies to understand the importance of each to the traceability of software engineering artifacts

Identification of early gene expression changes in primary cultured neurons treated with topoisomerase I poisons.

Topoisomerase 1 (TOP1) poisons like camptothecin (CPT) are currently used in cancer chemotherapy but these compounds can have damaging, off-target effects on neurons leading to cognitive, sensory and motor deficits. To understand the molecular basis for the enhanced sensitivity of neurons to CPT, we examined the effects of compounds that inhibit TOP1-CPT, actinomycin D (ActD) and β-lapachone (β-Lap)-on primary cultured rat motor (MN) and cortical (CN) neurons as well as fibroblasts. Neuronal cells expressed higher levels of Top1 mRNA than fibroblasts but transcript levels are reduced in all cell types after treatment with CPT. Microarray analysis was performed to identify differentially regulated transcripts in MNs in response to a brief exposure to CPT. Pathway analysis of the differentially expressed transcripts revealed activation of ERK and JNK signaling cascades in CPT-treated MNs. Immediate-early genes like Fos, Egr-1 and Gadd45b were upregulated in CPT-treated MNs. Fos mRNA levels were elevated in all cell types treated with CPT; Egr-1, Gadd45b and Dyrk3 transcript levels, however, increased in CPT-treated MNs and CNs but decreased in CPT-treated fibroblasts. These transcripts may represent new targets for the development of therapeutic agents that mitigate the off-target effects of chemotherapy on the nervous system

Toward Automating Requirements Satisfaction Assessment

This paper introduces the automation of satisfaction assessment: the process of determining the satisfaction mapping of natural language textual requirements to natural language design elements. Satisfaction assessment is useful because it assists in discovering unsatisfied requirements early in the lifecycle when such issues can be corrected with lower cost and impact than later. We define the basic terms and concepts for this process and explore the feasibility of developing baseline methods for its automation. This paper describes the satisfaction assessment approach algorithmically and then evaluates the effectiveness of two proposed information retrieval (IR) methods in two industrial studies - one based on a large dataset including a complete requirements specification and design specification for a NASA science instrument, and one based on a smaller dataset for an open source project management dataset. We found that both approaches have merit, and that the more sophisticated approach outperformed the simpler approach in terms of overall accuracy of the results

Will Johnny/Joanie Make a Good Software Engineer?: Are Course Grades Showing the Whole Picture?

Predicting future success of students as software engineers is an open research area. We posit that current grading means do not capture all the information that may predict whether students will become good software engineers. We use one such piece of information, traceability of project artifacts, to illustrate our argument. Traceability has been shown to be an indicator of software project quality in industry. We present the results of a case study of a University of Waterloo graduate-level software engineering course where traceability was examined as well as course grades (such as mid-term, project grade, etc.). We found no correlation between the presence of good traceability and any of the course grades, lending support to our argument