Assisting requirements analysts to find latent concerns with <Emphasis Type="Italic">REAssistant</Emphasis>

Textual requirements are very common in software projects. However, this format of requirements often keeps relevant concerns (e.g., performance, synchronization, data access, etc.) from the analyst’s view because their semantics are implicit in the text. Thus, analysts must carefully review requirements documents in order to identify key concerns and their effects. Concern mining tools based on NLP techniques can help in this activity. Nonetheless, existing tools cannot always detect all the crosscutting effects of a given concern on different requirements sections, as this detection requires a semantic analysis of the text. In this work, we describe an automated tool called REAssistant that supports the extraction of semantic information from textual use cases in order to reveal latent crosscutting concerns. To enable the analysis of use cases, we apply a tandem of advanced NLP techniques (e.g, dependency parsing, semantic role labeling, and domain actions) built on the UIMA framework, which generates different annotations for the use cases. Then, REAssistant allows analysts to query these annotations via concern-specific rules in order to identify all the effects of a given concern. The REAssistant tool has been evaluated with several case-studies, showing good results when compared to a manual identification of concerns and a third-party tool. In particular, the tool achieved a remarkable recall regarding the detection of crosscutting concern effects.     

Uncovering quality-attribute concerns in use case specifications via early aspect mining

Quality-attribute requirements describe constraints on the development and behavior of a software system, and their satisfaction is key for the success of a software project. Detecting and analyzing quality attributes in early development stages provides insights for system design, reduces risks, and ultimately improves the developers’ understanding of the system. A common problem, however, is that quality-attribute information tends to be understated in requirements specifications and scattered across several documents. Thus, making the quality attributes first-class citizens becomes usually a time-consuming task for analysts. Recent developments have made it possible to mine concerns semi-automatically from textual documents. Leveraging on these ideas, we present a semi-automated approach to identify latent quality attributes that works in two stages. First, a mining tool extracts early aspects from use cases, and then these aspects are processed to derive candidate quality attributes. This derivation is based on an ontology of quality-attribute scenarios. We have built a prototype tool called QAMiner to implement our approach. The evaluation of this tool in two case studies from the literature has shown interesting results. As main contribution, we argue that our approach can help analysts to skim requirements documents and quickly produce a list of potential quality attributes for the system.     

Identifying duplicate functionality in textual use cases by aligning semantic actions

Developing high-quality requirements specifications often demands a thoughtful analysis and an adequate level of expertise from analysts. Although requirements modeling techniques provide mechanisms for abstraction and clarity, fostering the reuse of shared functionality (e.g., via UML relationships for use cases), they are seldom employed in practice. A particular quality problem of textual requirements, such as use cases, is that of having duplicate pieces of functionality scattered across the specifications. Duplicate functionality can sometimes improve readability for end users, but hinders development-related tasks such as effort estimation, feature prioritization, and maintenance, among others. Unfortunately, inspecting textual requirements by hand in order to deal with redundant functionality can be an arduous, time-consuming, and error-prone activity for analysts. In this context, we introduce a novel approach called ReqAligner that aids analysts to spot signs of duplication in use cases in an automated fashion. To do so, ReqAligner combines several text processing techniques, such as a use case-aware classifier and a customized algorithm for sequence alignment. Essentially, the classifier converts the use cases into an abstract representation that consists of sequences of semantic actions, and then these sequences are compared pairwise in order to identify action matches, which become possible duplications. We have applied our technique to five real-world specifications, achieving promising results and identifying many sources of duplication in the use cases.     

Using semantic roles to improve text classification in the requirements domain

Engineering activities often produce considerable documentation as a by-product of the development process. Due to their complexity, technical analysts can benefit from text processing techniques able to identify concepts of interest and analyze deficiencies of the documents in an automated fashion. In practice, text sentences from the documentation are usually transformed to a vector space model, which is suitable for traditional machine learning classifiers. However, such transformations suffer from problems of synonyms and ambiguity that cause classification mistakes. For alleviating these problems, there has been a growing interest in the semantic enrichment of text. Unfortunately, using general-purpose thesaurus and encyclopedias to enrich technical documents belonging to a given domain (e.g. requirements engineering) often introduces noise and does not improve classification. In this work, we aim at boosting text classification by exploiting information about semantic roles. We have explored this approach when building a multi-label classifier for identifying special concepts, called domain actions, in textual software requirements. After evaluating various combinations of semantic roles and text classification algorithms, we found that this kind of semantically-enriched data leads to improvements of up to 18% in both precision and recall, when compared to non-enriched data. Our enrichment strategy based on semantic roles also allowed classifiers to reach acceptable accuracy levels with small training sets. Moreover, semantic roles outperformed Wikipedia- and WordNET-based enrichments, which failed to boost requirements classification with several techniques. These results drove the development of two requirements tools, which we successfully applied in the processing of textual use cases.     

Exploring Alternative Software Architecture Designs: A Planning Perspective

Software architecture designs give us blueprints to build systems, enabling key early decisions that can help us achieve a system's functional and quality-attribute requirements. Architectural decisions have far-reaching effects on development in terms of quality, time, and cost. Architects apply technical knowledge and experience to guide their decision making, choosing among multiple design solutions to find a reasonable balance of quality attributes such as performance, modifiability, or security. This is complex and time consuming because qualities can conflict and lead to trade-offs. A trade-off means that the improvement of one quality comes at the cost of degrading another for example, modifiability versus performance. The DesignBots framework supports architects in searching for design alternatives by capturing quality-attribute design concepts into a hierarchical, mixed-initiative planning model. Overall, this work reinforces the argument that Al-based tools can facilitate the design of architectures driven by quality-attribute issues.