Abstract: requirement traceability is an important and costly task that creates trace links from requirements to different software artifacts. These trace links can help engineers reduce the time and complexity of software maintenance. The information retrieval (IR) technique has been widely used in requirement traceability. It uses the textual similarity between software artifacts to create links. However, if two artifacts do not share or share only a small number of words, the performance of the IR can be very poor. Some methods have been developed to enhance the IR by considering relations between target artifacts, but they have been limited to code rather than to other types of target artifacts. To overcome this limitation, we propose an automatic method that combines the IR method with the close relations between target artifacts. Specifically, we leverage close relations between target artifacts rather than just text matching from requirements to target artifacts. Moreover, the method is not limited to the type of target artifacts when considering the relations between target artifacts. We conduct experiments on five public datasets and take account of trace links between requirements and different types of software artifacts. Results show that under the same recall, the precisions on the five datasets improve by 40%, 8%, 20%, 4%, and 6%, respectively, compared with the baseline method. The precision on the five datasets improves by an average of 15.6%, showing that our method outperforms the baseline method when working under the same conditions.

通过分析目标制品间的紧密关系改进基于信息检索的需求追踪恢复

[1]Abadi A, Nisenson M, Simionovici Y, 2008. A traceability technique for specifications. Proc 16^th IEEE Int Conf on Program Comprehension, p.103-112.

[2]Antoniol G, Canfora G, Casazza G, et al., 2002. Recovering traceability links between code and documentation. IEEE Trans Softw Eng, 28(10):970-983.

[3]Armstrong BT, 2013. Can Clustering Improve Requirements Traceability? A Tracelab-Enabled Study. MS Thesis, California Polytechnic State University, San Luis Obispo, USA.

[4]Burgstaller B, Egyed A, 2010. Understanding where requirements are implemented. Proc IEEE Int Conf on Software Maintenance, p.1-5.

[5]Chen XF, Grundy J, 2011. Improving automated documentation to code traceability by combining retrieval techniques. Proc 26^th IEEE/ACM Int Conf on Automated Software Engineering, p.223-232.

[6]Dekhtyar A, Hayes JH, Larsen J, 2007. Make the most of your time: how should the analyst work with automated traceability tools? Proc 3^rd Int Workshop on Predictor Models in Software Engineering, Article 4.

[7]de Lucia A, Fasano F, Oliveto R, et al., 2005. ADAMS Re-Trace: a traceability recovery tool. Proc 9^th European Conf on Software Maintenance and Reengineering, p.32-41.

[8]Dietrich T, Cleland-Huang J, Shin Y, 2013. Learning effective query transformations for enhanced requirements trace retrieval. Proc 28^th IEEE/ACM Int Conf on Automated Software Engineering, p.586-591.

[9]Du TB, Shen GH, Huang ZQ, et al., 2020. Automatic traceability link recovery via active learning. Front Inform Technol Electron Eng, 21(8):1217-1225.

[10]Gethers M, Oliveto R, Poshyvanyk D, et al., 2011. On integrating orthogonal information retrieval methods to improve traceability recovery. Proc 27^th IEEE Int Conf on Software Maintenance, p.133-142.

[11]Ghannem A, Hamdi MS, Kessentini M, et al., 2017. Search-based requirements traceability recovery: a multi-objective approach. Proc IEEE Congress on Evolutionary Computation, p.1183-1190.

[12]Gotel OCZ, Finkelstein CW, 1994. An analysis of the requirements traceability problem. Proc IEEE Int Conf on Requirements Engineering, p.94-101.

[13]Guo J, Cheng JH, Cleland-Huang J, 2017. Semantically enhanced software traceability using deep learning techniques. Proc IEEE/ACM 39^th Int Conf on Software Engineering, p.3-14.

[14]Hayes JH, 2012. Be the change you want to see in requirements engineering: a letter to myself. Dagstuhl Seminar, Requirements Management-Novel Perspectives and Challenges (12442).

[15]Hayes JH, Dekhtyar A, Sundaram SK, 2005. Improving after-the-fact tracing and mapping: supporting software quality predictions. IEEE Softw, 22(6):30-37.

[16]Hayes JH, Dekhtyar A, Sundaram SK, 2006. Advancing candidate link generation for requirements tracing: the study of methods. IEEE Trans Softw Eng, 32(1):4-19.

[17]Hayes JH, Dekhtyar A, Sundaram SK, et al., 2007. REquirements TRacing On target (RETRO): improving software maintenance through traceability recovery. Innov Syst Softw Eng, 3(3):193-202.

[18]Keenan E, Czauderna A, Leach G, et al., 2012. TraceLab: an experimental workbench for equipping researchers to innovate, synthesize, and comparatively evaluate traceability solutions. Proc 34^th Int Conf on Software Engineering, p.1375-1378.

[19]Kuang HY, Nie J, Hu H, et al., 2017. Analyzing closeness of code dependencies for improving IR-based traceability recovery. Proc IEEE 24^th Int Conf on Software Analysis, Evolution and Reengineering, p.68-78.

[20]Leuser J, Ott D, 2010. Tackling semi-automatic trace recovery for large specifications. Proc 16^th Int Working Conf on Requirements Engineering: Foundation for Software Quality, p.203-217.

[21]Lin J, Lin CC, Cleland-Huang J, et al., 2006. Poirot: a distributed tool supporting enterprise-wide automated traceability. Proc 14^th IEEE Int Requirements Engineering Conf, p.363-364.

[22]Mahmoud A, Niu N, 2011. TraCter: a tool for candidate traceability link clustering. Proc IEEE 19^th Int Requirements Engineering Conf, p.335-336.

[23]Mahmoud A, Niu N, 2013. Supporting requirements traceability through refactoring. Proc 21^st IEEE Int Requirements Engineering Conf, p.32-41.

[24]Mahmoud A, Niu N, 2014. Supporting requirements to code traceability through refactoring. Requir Eng, 19(3):309-329.

[25]Marcus A, Maletic JI, 2003. Recovering documentation-to-source-code traceability links using latent semantic indexing. Proc 25^th Int Conf on Software Engineering, p.125-135.

[26]Oliveto R, Gethers M, Poshyvanyk D, et al., 2010. On the equivalence of information retrieval methods for automated traceability link recovery. Proc IEEE 18^th Int Conf on Program Comprehension, p.68-71.

[27]Panichella A, McMillan C, Moritz E, et al., 2013. When and how using structural information to improve IR-based traceability recovery. Proc 17^th European Conf on Software Maintenance and Reengineering, p.199-208.

[28]Wang XB, Lai GH, Liu C, 2009. Recovering relationships between documentation and source code based on the characteristics of software engineering. Electron Notes Theor Comput Sci, 243:121-137.

[29]Zhao T, Cao QH, Sun Q, 2017. An improved approach to traceability recovery based on word embeddings. Proc 24^th Asia-Pacific Software Engineering Conf, p.81-89.

[30]Zou XC, Settimi R, Cleland-Huang J, 2006. Phrasing in dynamic requirements trace retrieval. Proc 30^th Annual Int Computer Software and Applications Conf, p.265-272.

[31]Zou XC, Settimi R, Cleland-Huang J, 2010. Improving automated requirements trace retrieval: a study of term-based enhancement methods. Empir Softw Eng, 15(2):119-146.