Recovering Traceability Links in Requirements Documents

Proceedings of the 19th Conference on Computational Language Learning, pages 237–246, Beijing, China, July 30-31, 2015. c⃝2015 Association for Computational Linguistics Recovering Traceability Links in Requirements Documents Zeheng Li Mingrui Chen LiGuo Huang Department of Computer Science & Engineering Southern Methodist University Dallas, TX 75275-0122 {zehengl,mingruic,lghuang}@smu.edu Vincent Ng Human Language Technology Institute University of Texas at Dallas Richardson, TX 75083-0688 vince@hlt.utdallas.edu Abstract Software system development is guided by the evolution of requirements. In this pa- per, we address the task of requirements traceability, which is concerned with pro- viding bi-directional traceability between various requirements, enabling users to find the origin of each requirement and track every change made to it. We pro- pose a knowledge-rich approach to the task, where we extend a supervised base- line system with (1) additional training instances derived from human-provided annotator rationales; and (2) additional features derived from a hand-built ontol- ogy. Experiments demonstrate that our ap- proach yields a relative error reduction of 11.1–19.7%. 1 Introduction Software system development is guided by the evolution and refinement of requirements. Re- quirements specifications, which are mostly doc- umented using natural language, are refined with additional design details and implementation in- formation as the development life cycle pro- gresses. A crucial task throughout the entire de- velopment life cycle is requirements traceability, which is concerned with linking requirements in which one is a refinement of the other. Specifically, one is given a set of high-level (coarse-grained) requirements and a set of low- level (fine-grained) requirements, and the goal of requirements traceability is to find for each high- level requirement all the low-level requirements that refine it. Note that the resulting mapping be- tween high- and low-level requirements is many- to-many, because a low-level requirement can po- tentially refine more than one high-level require- ment. As an example, consider the three high- level requirements and two low-level requirements shown in Figure 1 about the well-known Pine email system. In this example, three traceabil- ity links should be established: (1) HR01 is re- fined by UC01 (because UC01 specifies the short- cut key for saving an entry in the address book); (2) HR02 is refined by UC01 (because UC01 spec- ifies how to store contacts in the address book); and (3) HR03 is refined by UC02 (because both of them are concerned with the help system). From a text mining perspective, requirements traceability is a very challenging task. First, there could be abundant information irrelevant to the establishment of a link in one or both of the re- quirements. For instance, all the information un- der the Description section in UC01 is irrelevant to the establishment of the link between UC01 and HR02. Worse still, as the goal is to induce a many-to-many mapping, information irrelevant to the establishment of one link could be rele- vant to the establishment of another link involv- ing the same requirement. For instance, while the Description section is irrelevant when linking UC01 and HR02, it is crucial for linking UC01 and HR01. Above …

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