Utopia :: essays research papers

Utopia Utopia represents the notion of an ideal commonwealth, as the expression in a fable of what would at once be a standard and touchstone for social and political regulations. Hytholoday begins to talk of his adventures, and to describe how in the course of them he had come across many interesting communities, among them the commonwealth, whose custom on laws might well serve as examples to European Countries. By directs comments on the evils and miseries prevalent in England and Europe with an analysis of their causes and suggestions for their remedies. When More and Giles discover how widely Hythloday had traveled and realized the depth of his understanding of the governments of many nations, the propose that his knowledge is too valuable to waste and he ought to as a counselor in order to employ his knowledge in service of mankind. But Hythloday address so far on the reasons for his reluctance to undertake such employment. He does not believe that , as things stand, his advice would be accepted. The majority of those presently sitting in royal councils invariably practice a system of flattering toward their superiors and of personal aggrandizement and would surely override his idealistic and philosophical proposals. Hythloday proceeds with the argument to a critical analysis of patterns of law, government, economic and more, among European nation, particularly in England. He criticism are directed specifically at the severity of the penal code, the terrible inequities in the distribution of wealth, the unequal participation in productive labor and so on. After which Hythloday gives an account of the whole life pattern of the Utopians. Hythloday argued that a human life is of more worth than money and that is unreasonable to punish equally the taking of a man ¡Ã‚ ¦s life and his property. The Utopians have not only eliminated money from their economy, they have devised psychological methods to teach their people to despise those precious metals that are used for money in other countries. The concept of the romantic love or the grand passion is not brought into the discussion. Though it is evident throughout Utopia that the family relationship is emphatically advocated and the permanence of matrimony is supported. As the final statement, More presents a puzzle that he led to a major controversy over whether or not to contribute the plan for a society such us he had described in the book. The concept of Utopists, was to create a design for a ideal society, one must then discard the existing one and start a new afresh one.

Animal Farm Analysis Essay Essay

The Tragedy Of Oppression â€Å"Among those who dislike oppression are many who like to oppress†. These are the words that rolled off the French General Napoleon Bonaparte’s tongue. This relates to Animal Farm because all of the farm animals do not like oppression but they have a bunch of oppressors among them. The pigs control the farm animals in every way possible and frankly they are sick and tired of it. Animal Farm by George Orwell shows that oppression is at the heart of so much sorrow in life, and that the abuse of power is at the heart of oppression. Napoleons violence and Squealers mind-control are the two forms of power and abuse that keep the poor animals oppressed. Napoleon was violent in many ways. First of all, he used his power to raise an army of dogs to become mean and violent just like him. Napoleon had the dogs under his wing; they would do whatever he told them to do. For example, Napoleon was trying to get the animals to admit to working with snowball. When and if they did admit to this he would command the dogs to kill them. This is one of many examples of how Napoleon uses his power to abuse the farm animals. Napoleon hated Snowball; they disagreed on everything so he definitely didn’t want any of his farm animals working with his enemy. That’s not all Napoleon had the dogs, or in other words his â€Å"slaves† do. Another example of violence is when Napoleon commanded the dogs to chase Snowball off of the farm. He didn’t like the idea of sharing the power with Snowball; he wanted the power all to himself! He tried to control everyone through violence and fear. As a result of this power struggle he abused not only the farm animals but also the dogs by trying to control them. Napoleon verbally, mentally and physically abused everyone in his path. Squealer is also guilty of abusing his power. Squealer’s choice of abuse is mind control. He is capable of making a bad situation seem as if it were meant to be. Like the time when the farm animals got suspicious when the pigs started sleeping in beds. Everyone on the farm knows that sleeping in bed goes against the commandment that states, â€Å"No animal shall sleep in a bed† When the animals built up the courage to ask the pigs what was going on, the pigs simply stated â€Å"A bed merely means a place to sleep in. The rule was against sheets, which are a human invention. We have removed the sheets from the farmhouse beds, and sleep between blankets. You would not have us too tired to carry out our duties? Surely none of you wishes to see Jones back?†(Orwell67). This is an  example of slippery slope because in other words Squealer is telling the animals that if the pigs aren’t allowed to sleep in beds then they will not be able to do there duties because they will not get a good night sleep and if they cant do their duties then eventually Jones will come back. Squealer does things like this all of the time. He is know n for verbally abusing the farm animals by controlling their minds. This all leads up to one thing, oppression. Oppression is caused by the abuse of power. Napoleon wanted to be in control of everything and everyone because he was a very selfish and controlling pig. His need to be this way caused severe oppression on the farm. The farm animals were all unhappy and felt like there was no way out. Anytime they confronted the pigs, they were always given some excuse or reason why things were not the way they were supposed to be. Boxer was the hardest worker on the farm, yet he got treated the most unfair. He might not have been the most intelligent of the animals but he sure was strong and worked more than any of the others. One day, Boxer collapsed while pulling stone for the windmill. The pigs said that he would be taken to a hospital. When a cart arrived to pick Boxer up the farm animals got suspicious because the cart was sent from the slaughterhouse. Squealer quickly denounced the rumor saying that the hospital had bought the cart from the slaughterhouse and never painted over the writing. The animals were relieved after hearing the â€Å"good news† until squealer announced that Boxer had died in the hospital. â€Å"I will work harder† and â€Å"Napoleon is always right†(Orwell 39). were Boxers two slogans. Boxer thought of Napoleon as a role model and all Napoleon thought of Boxer as, was one of his many workers. Boxer loved Napoleon. It is very sad that he was never appreciated or loved in return by Napoleon. At least Boxer will always be in the hearts of the farm animals because he was truly loved by them. This was a great and unnecessary loss, which caused much sorrow to the farm animals. This clearly shows that oppression can cause great sorrow. The abuse of power is the cause of great sorrow for many people all around the world. The Novella Animal Farm by George Orwell proves this in the life of the farm animals. Napoleon caused a lot of unhappiness and sorrow to the farm animals, which lead to great oppression on the farm. In order to keep h is power, Napoleon used violence to get his way. He used Squealer to play mind games on the animals to keep his power and to trick them into believing his lies. Oppression is a disheartening reality that affected the farm animals and continues to affect the lives of many people today.

Jane Austen s Pride And Prejudice - 1589 Words

Introduction Jane Austen’s’ Pride and Prejudice was published in 1813. The setting of the novel was based in England. During the 18th century, society’s main focus was on social status, courtship, and marriage. Pride and Prejudice depicts the commotion of a town called Longbourn by the arrival of Mr. Bingley, a wealthy man. At the beginning of the novel, Mrs. Bennet is trying to marry one of her five daughters-Jane, Elizabeth, Mary, Kitty, or Lydia-to Bingley. When Mr. Bingley meets Jane at a ball, he is immediately attracted to her. Yet Mr. Darcy, Bingley’s friend, is increasingly hostile towards Elizabeth. Jane and Bingley continue to get more acquainted with each other, while Darcy begins to become attracted to Elizabeth. Throughout the novel, Mr. Bingley departs for London on business. Elizabeth is sure that Mr. Darcy and Caroline, Bingley’s sister, are trying to separate Jane and Bingley. Mr. Darcy asks Elizabeth hand in marriage, but Elizabeth says no because she learns that Darcy advised Bingley not to marry Jane. Mr. Darcy then proceeds to right a letter explaining everything to Elizabeth. Elizabeth immediately develops guilt for misjudging Darcy, therefore creating a shift in the novel. The Bingley’s, Mr. Darcy, Jane, and Elizabeth are prime examples of what Pride and Preju dice analyzes. The novel examines how life was in the upper society of England. Jane Austen is describing how people courted each other back then, with money involved. As the story develops,Show MoreRelatedJane Austen s Pride And Prejudice1231 Words   |  5 Pagesfinancial stability. In the novel Pride and Prejudice, Jane Austen states that the desire for better social connections interferes with the workings of love through the relationship between Darcy and Elizabeth to criticize the social class structure of the 19th century. Anxieties about social connections or the desire for better social connections, interfere with the workings of love. Darcy and Elizabeth s realization of a mutual and tender love seems to imply that Jane Austen views love as something independentRead MoreJane Austen s Pride And Prejudice1294 Words   |  6 PagesJane Austen s exceptional novel Pride and Prejudice has been depicted as a classic that is as much a social study on class, marriage and gender as it is a romantic tale. It is an amusing representation of the social atmosphere of the late eighteenth and mid nineteenth century England, and it is primarily required with courtship rituals of the English high class. The novel is more than a romantic tale, however through Austen s subtle, and ironic style, it addresses gender, class, and marriageRead MoreJane Austen s Pride And Prejudice992 Words   |  4 Pages It is unfortunate that many people tend to dismiss Jane Austen’s novel, Pride and Prejudice, as simply a roman tic love story, even labeling it a â€Å"chick flick.† Upon a shallow reading, it may appear to be such, but a closer look at the novel reveals so much more embedded in the story. In addition to describing the entertaining relationship between Elizabeth and Mr. Darcy, the novel serves to forward Austen s personal values and ideas. Furthermore, there is one issue of her era that she particularlyRead MoreJane Austen s Pride And Prejudice1138 Words   |  5 PagesPride and Prejudice is a novel about the superficiality of marriage during the late 19th and early 20th century, which largely influenced the decisions made by individuals, based on connections and social rankings. The novel takes its characters through various changes influenced by their decision to or rather not to marry certain individuals. It begins not by a man desiring to marry for love, but by a mother who desires nothing more than to marry her daughters well. As the novel develops, Jane AustenRead MoreJane A usten s Pride And Prejudice1211 Words   |  5 PagesJane Austen’s Pride and Prejudice was greatly influenced by the time period in which it was written, This novel follows the story of Elizabeth Bennet and her sisters as they are faced with marriage proposals. The marriage and roles of women in this time period are shown throughout this story. During the time Austen was writing this novel, a woman’s role for her family changed. Daughters started to become a way for their family to achieve more money. Because their family depended on this financialRead MoreJane Austen s Pride And Prejudice1675 Words   |  7 PagesIn Jane Austen’s Pride and Prejudice, she has specific criteria that her characters follow when choosing their mates. In today’s society, most couples still follow these criteria and more when choosing their ideal mate. What are these important criteria that Austen’s characters consider when choosing a mate? For Austen, the important cr iteria that she has for choosing a mate are that couples are personally compatible, they are in love with each other, and they must have a good moral character. Read MoreJane Austen s Pride And Prejudice1678 Words   |  7 PagesAfter reading Jane Austen’s most popular piece of work, the effects of the high societal expectations can be acknowledged through viewing the lives of the Bennet family and friends and noting such effects. Through the examination of the characters in Pride and Prejudice it is easily deciphered between marriages based upon true love and marriage based upon the expectations of society. Society’s main goal for woman in the Victorian era was marriage. As seen many in Pride and Prejudice, marriage wasRead MoreJane Austen s Pride And Prejudice1434 Words   |  6 PagesJane Austen’s Pride and Prejudice was considered a radical novel back in 1813 when she wrote and published the piece. It is a social commentary on the treatment and societal standards of women, as well marriage expectations at the turn of the 19th century. Austen criticizes the patriarchal society, materialism, double standards of men and women by centering the book around Elizabeth Bennett, a young woman of decent means who does not understand the reason for the pressure to find a suitable husbandRead MoreJane Austen s Pride And Prejudice1468 Words   |  6 Pagesestablished over time. In Jane Austen s novel, Pride and Prejudice Elizabeth Bennet is the main character who is a lady in the Regency Era. Elizabeth lives in Longbourn with her parents, Mr and Mrs Bennet and her four sisters. In the beginning of the novel, Elizabeth s prejudice mindset and strong opinion blinds her from realizations happening around her. Soon, Elizabeth s prejudice disappears allowing her to open up and fall in love. Throughout Jane Austen s novel, Pride and Prejudice, Elizabeth growsRead MoreJane Austen s Pride And Prejudice1649 Words   |  7 PagesIn her novel, Pride and Prejudice, Jane Austen is pre-occupied with the theme of marriage. Marriage is a central issue of a woman’s life but it was even more crucial for the women of her society where women were largely dependent on the men in their lives. As a result, women pursued socio-economic stability through marriage. However, it is clear through the novel that Austen did not agree with this part of her society. In Pride and Prejudice, she gives preference to a marriage which is based on love

Effects Of Alcohol On A Public Health Perspective

Drinkaware Campaign Within England more than 9 million people drink more than the recommended daily limits, and as recorded in 2012 there were 6,490 alcohol related deaths (NHS, 2012), a 19% increase compared to those recorded statistics in 2001 (NHS, 2012). This shows a major cause for concern within England to date, driving the promotion of the Drinkaware campaign to tackle the problem. Statistics show that alcohol composes of 10% of the UK burden of disease and death, making alcohol one of the three biggest lifestyle risk factors for disease and death in the UK, after smoking and obesity (HOC, 2014). It is estimated that around 7.5 million people are still unaware of the impact/damage that poor drinking habits cause to their bodies,†¦show more content†¦Excessive alcohol consumption is a major source of morbidity and premature death in the UK. The World Health Organisation lists alcohol as the third leading risk factor for premature death in developed countries, with only tobacco and blood pressure causing more premature death and disability (The Royal College of Nursing, 2014). Alcohol is widely known as being part of many people’s family and social life, and in reality consumers are unaware that it is however a poison and an addictive substance. Alcohol can cause physiological and psychological harm in users and has wider social consequences (Reh m J and Mathers C, 2009) What is the Drinkaware campaign? â€Å"Drinkaware is a charitable organisation which aims to get people to think differently about alcohol. The entire focus of the campaigns is to get people to understand the harm that alcohol can have on people’s health, families and others around them† (Drinkaware, 2006). â€Å"Its vision is of a society where alcohol is enjoyed responsibly, where its potent ion for harm is reduced and where costumers are supported in practical ways to make informed decisions about their drinking†. (Drinkaware, 2006) Differently to other health promotion campaigns, the Drinkaware campaign is a long running campaign(s),

The Financial Crisis Of Greece - 2156 Words

Introduction Since the 1990s, the Greek economy has been suffering from a deficit in its accounts due to years of funds mismanagement and poor fiscal policies. However, the task of dealing with the issue was passed on from one executive to the next, hence exacerbating the problem. When Greece saw the possibility of joining the European Union in 1981, lack of transparency and misreported figures in its bookkeeping system allowed the country to meet the criteria and become a new member of the Eurozone. As investors were confident on the stability of the Euro, Greece was able to borrow cheaply from the European financial markets and sustain an expansionary policy between 2001 and 2007. Nonetheless, the outburst of global financial crisis†¦show more content†¦From 2001, low-interest borrowings allowed the Hellenic country to increase government spending and investments. Therefore, as economy expanded so did GDP. Fig.1 shows how the real GDP, which consider only changes in output but not in prices, went from â‚ ¬45millions in 2001 to â‚ ¬65millions in 2008. Yet, this period of growth was short lived: failure in collecting tax revenues gradually widen the Greek budget deficit and intensified the effects of the 2008/9 financial crisis. (Source: Trading economics | National Statistical Service of Greece, 2015) Fig.1 As a result of the global financial distress, the GDP growth rate dropped to -4.3% and caused the recession in Greece, and similarly in the whole Eurozone. Although other European countries succeeded in limiting the economic damages, Greece’s austerity policies and debit crisis protracted the economic downturn wherein the GDP growth rate bottomed at -9% in 2011 (Fig.2). (Source: The World Bank, 2016) Fig.2 While the rest of the Eurozone registered a negative output gap that remained relatively low at around -3%, Greece’s GDP gap has been particularly wide and showed an actual output well below potential. As seen in Fig. 3, having an inflationary output gap until 2009 and then large recessionary gap suggest a leftward shift in the aggregate demand curve. (Source: Organization for Economic Co-operation and Development, 2015)

Part of Speech Recognizer Free Essays

Improving Identi?er Informativeness using Part of Speech Information Dave Binkley Matthew Hearn Dawn Lawrie Loyola University Maryland Baltimore MD 21210-2699, USA {binkley, lawrie}@cs. loyola. edu, mthearn@loyola. We will write a custom essay sample on Part of Speech Recognizer or any similar topic only for you Order Now edu Keywords: source code analysis tools, natural language processing, program comprehension, identi?er analysis Abstract Recent software development tools have exploited the mining of natural language information found within software and its supporting documentation. To make the most of this information, researchers have drawn upon the work of the natural language processing community for tools and techniques. One such tool provides part-of-speech information, which ?nds application in improving the searching of software repositories and extracting domain information found in identi?ers. Unfortunately, the natural language found is software differs from that found in standard prose. This difference potentially limits the effectiveness of off-the-shelf tools. The presented empirical investigation ?nds that this limitation can be partially overcome, resulting in a tagger that is up to 88% accurate when applied to source code identi?ers. The investigation then uses the improved part-of-speech information to tag a large corpus of over 145,000 ?eld names. From patterns in the tags several rules emerge that seek to improve structure-?eld naming. Source Part of Extract Split Apply Source ? Code ? Field ? Field ? ? Speech Template Code Mark-up Tagging Names Names Figure 1. Process for POS tagging of ?eld names. The text available in source-code artifacts, in particular a program’s identi?ers, has a very different structure. For example the words of an identi?er rarely form a grammatically correct sentence. This raises an interesting question: can an existing POS tagger be made to work well on the natural language found in source code? Better POS information would aid existing techniques that have used limited POS information to successfully improve retrieval results from software repositories [1, 11] and have also investigated the comprehensibility of source code identi?ers [4, 6]. Fortunately, machine learning techniques are robust and, as reported in Section 2, good results are obtained using several sentence forming templates. This initial investigation also suggest rules speci?c for software that would improve tagging. For example the type of a declared variable can be factored into its tags. As an example application of POS tagging for source code, the tagger is then used to tag over 145,000 structure?eld names. Equivalence classes of tags are then examined to produce rules for the automatic identi?cation of poor names (as described in Section 3) and suggest improved names, which is left to future work. 1 Introduction Software engineering can bene?t from leveraging tools and techniques of other disciplines. Traditionally, natural language processing (NLP) tools solve problems by processing the natural language found in documents such as news articles and web pages. One such NLP tool is a partof-speech (POS) tagger. Tagging is, for example, crucial to the Named-Entity Recognition [3], which enables information about a person to be tracked within and across documents. Many POS taggers are built using machine learning based on newswire training data. Conventional wisdom is that these taggers work well on the newswire and similar artifacts; however, their effectiveness degrades as the input moves further away from the highly structured sentences found in traditional newswire articles. 1 2 Part-of-Speech Tagging Before a POS tagger’s output can be used as input to down stream SE tools, the POS tagger itself needs to be vetted. This section describes an experiment performed to test the accuracy of POS tagging on ?eld names mined from source code. The process used for mining and tagging the ?elds is ?rst described, followed by the empirical results from the experiment. Figure 1 shows the pipeline used for the POS tagging of ?eld names. On the left, the input to the pipeline is mode=â€Å"space†/ (683 came from C++ ?les and 817 from Java ?les). A human accessor (and university student majoring in English) tagged the 1500 ?eld names with POS information producing the oracle set. This oracle set is used to evaluate the accuracy of automatic tagging techniques when applied to the test set. Preliminary study of the Stanford tagger indicates that it needed guidance when tagging ?eld names. Following the work of Abebe and Tonella [1], four templates were used to provide this guidance. Each template includes a slot into which the split ?eld name is inserted. Their accuracy is then evaluated using the oracle set. †¢ †¢ †¢ †¢ Sentence Template: List Item Template: Verb Template: Noun Template: . – Please, . is a thing . Figure 2. XML queries for extracting C++ and Java ?elds from srcML. source code. This is then marked up using XML tags by srcML [5] to identify various syntactic categories. Third, ?eld names are extracted from the marked-up source using XPath queries. Figure 2 shows the queries for C++ and Java. The fourth stage splits ?eld names by replacing underscores with spaces and inserting a space where the case changes from lowercase to uppercase. For example, the names spongeBob and sponge bob become sponge bob. After splitting, all characters are shifted to lowercase. This stage also ?lters names so that only those that consist entirely of dictionary words are retained. Filtering uses Debian’s American (6-2) dictionary package, which consists of the 98,569 words from Kevin Atkinson’s SCOWL word lists that have size 10 through 50 [2]. This dictionary includes some common abbreviations, which are thus included in the ?nal data set. Future work will obviate the need for ?ltering through vocabulary normalization in which non-words are split into their abbreviations and then expanded to their natural language equivalents [9]. The ?fth stage applies a set of templates (described below) to each separated ?eld name. Each template effectively wraps the words of the ?eld name in an attempt to improve the performance of the POS tagger. Finally, POS tagging is performed by Version 1. 6 of the Stanford Log-linear POS Tagger [12]. The default options are used including the pretrained bidirectional model [10]. The remainder of this section considers empirical results concerning the effectiveness of the tagging pipeline. A total of 145,163 ?eld names were mined from 10,985 C++ ?les and 9,614 Java ?les found in 171 programs. From this full data set, 1500 names were randomly chosen as a test set 2 The Sentence Template, the simplest of the four, considers the identi?er itself to be a â€Å"sentence† by appending a period to the split ?eld. The List Item Template exploits the tagger having learned about POS information found in the sentence fragments used in lists. The Verb Template tries to encourage the tagger to treat the ?eld name as a verb or a verb phrase by pre?xing it with â€Å"Please,† since usually a command follows. Finally, the Noun Template tries to encourage the tagger to treat the ?eld as a noun by post?xing it with â€Å"is a thing† as was done by Abebe and Tonella [1]. Table 1 shows the accuracy of using each template applied to the test set with the output compared to the oracle. The major diagonal represents each technique in isolation while the remaining entries require two techniques to agree and thus lowering the percentage. The similarity of the percentages in a column gives an indication of how similar the set of correctly tagged names is for two techniques. For example, considering Sentence Template, Verb Template has the lowest overlap of the remaining three as indicated by it’s joint percentage of 71. 7%. Overall, the List Item Template performs the best, and the Sentence Template and Noun Template produce essentially identical results getting the correct tagging on nearly all the same ?elds. Perhaps unsurprising, the Verb Template performs the worst. Nonetheless, it is interesting that this template does produce the correct output on 3. 2% of the ?elds where no other template succeeds. As shown in Table 2 overall at least one template correctly tagged 88% of the test set. This suggests that it may be possible to combine these results, perhaps using machine learning, to produce higher accuracy than achieved using the individual templates. Although 88% is lower than the 97% achieved by natural language taggers on the newswire data, the performance is still quite high considering the lack of context provided by the words of a single structure ?eld. Sentence List Item Verb Noun Sentence 79. 1% 76. 5 71. 7% 77. 0% List Item 76. 5% 81. 7% 71. 0% 76. 0% Verb 71. 7% 71. 0% 76. 0% 70. 8% Noun 77. 0% 76. 0% 70. 8% 78. 7% this context is used to represent a current state, and is therefore not confusing. Rule 1 Non-boolean ?eld names should never contain a present tense verb * * ? * * Table 1. Each percentage is the percent of correctly tagged ?eld names using both the row and column technique; thus the major diagonal represent each technique independently. Correct in all templates Correct in at least one template 68. 9% 88. 0% Table 2. Correctly tagged identi?ers As illustrated in the next section, the identi?cation is suf?ciently accurate for use by downstream consumer applications. 3 Rules for Improving Field Names As an example application of POS tagging for source code, the 145,163 ?eld names of the full data set were tagged using the List Item Template, which showed the best performance in Table 1. The resulting tags were then used to form equivalence classes of ?eld names. Analysis of these classes led to four rules for improving the names of structure ?elds. Rule violations can be automatically identi?ed using POS tagging. Further, as illustrated in the examples, by mining the source code it is possible to suggest potential replacements. The assumption behind each rule is that high quality ?eld names will provide better conceptual information, which aids an engineer in the task of forming a mental understanding of the code. Correct part-of-speech information can help inform the naming of identi?ers, a process that is essential in communicating intent to future programmers. Each rule is ?rst informally introduced and then formalized. After each rule, the percentage of ?elds that violate the rule is given. Finally, some rules are followed by a discussion of rule exceptions or related notions. The ?rst rule observes that ?eld names represent objects not actions; thus they should avoid present-tense verbs. For example, the ?eld name create mp4, clearly implies an action, which is unlikely the intent (unless perhaps the ?eld represent a function pointer). Inspection of the source code reveals that this ?eld holds the desired mp4 video stream container type. Based on the context of its use, a better, less ambiguous name for this identi?er is created mp4 container type, which includes the past-tense verb created. A notable exception to this is ?elds of type boolean, like, for example, is logged in where the present tense of the verb â€Å"to be† is used. A present tense verb in 3 Violations detected: 27,743 (19. 1% of ?eld names) Looking at the violations of Rule 1 one pattern that emerges suggests an improvement to the POS tagger that would better specialize it to source code. A pattern that frequently occurs in GUI programming ?nds verbs used as adjectives when describing GUI elements such as buttons. Recognizing such ?elds based on their type should improve tagger accuracy. Consider the ?elds delete button and to a lesser extent continue box. In isolation these appears to represent actions. However they actually represent GUI elements. Thus, a special context-sensitive case in the POS tagger would tag such verbs as adjectives. The second rule considers ?eld names that contain only a verb. For example the ?eld name recycle. This name communicates little to a programmer unfamiliar with the code. Examination of the source code reveals that this variable is an integer and, based on the comments, it counts the â€Å"number of things recycled. † While this meaning can be inferred from the declaration and the comments surrounding it, ?eld name uses often occur far from their eclaration, reducing the value of the declared type and supporting comments. A potential ?x in this case is to change the name to recycled count or things recycled. Both alternatives improve the clarity of the name. Rule 2 Field names should never be only a verb ? ? or ? ? Violations detected: 4,661 (3. 2% ?eld names identi?ers) The third rule considers ?eld names that contain only an adjective. While adjectives are useful when used with a noun, an adjective alone relies too much on the type of the variable to fully explain its use. For example, consider the identi?er interesting. In this case, the declared type of â€Å"list† provides the insight that this ?eld holds a list of â€Å"interesting† items. Replacing this ?eld with interesting list or interesting items should improve code understanding. Rule 3 Field names should never be only an adjective ? Violations detected: 5,487 (3. 8% ?eld names identi?ers) An interesting exception to this rule occurs with data structures where the ?eld name has an established conventional meaning. For example, when naming the next node in a linked list, next is commonly accepted. Other similar common names include â€Å"previous† and â€Å"current. † The ?nal rule deals with ?eld names for booleans. Boolean variables represent a state that is or is not and this notion needs to be obvious in the name. The identi?er deleted offers a good example. By itself there is no way to know for sure what is being represented. Is this a pointer to a deleted thing? Is it a count of deleted things? Source code inspection reveals that such boolean variables tend to represent whether or not something is deleted. Thus a potential improved names include is deleted or was deleted. Rule 4 Boolean ?eld names should contain third person forms of the verb â€Å"to be† or the auxiliary verb â€Å"should† * ? is | was | should * 5 Summary This paper presents the results on an experiment into the accuracy of the Stanford Log-linear POS Tagger applied to ?eld names. The best template, List Item, has an accuracy of 81. 7%. If an optimal combination of the four templates were used the accuracy rises to 88%. These POS tags were then used to develop ?eld name formation rules that 28. 9% of the identi?ers violated. Thus the tagging can be used to support improved naming. Looking forward, two avenues of future work include automating this improvement and enhancing POS tagging for source code. For the ?rst, the source code would be mined for related terms to be used in suggested improved names. The second would explore training a POS tagger using, for example, the machine learning technique domain adaptation [8], which emphasize the text in the training that is most similar to identi?ers to produce a POS tagger for identi?ers. 6 Acknowledgments Special thanks to Mike Collard for his help with srcML and the XPath queries and Phil Hearn for his help with creating the oracle set. Support for this work was provided by NSF grant CCF 0916081. Violations detected: 5,487 (3. 8% ?eld names identi?ers) Simply adding â€Å"is† or â€Å"was† to booleans does not guarantee a ?x to the problem. For example, take a boolean variable that indicates whether something should be allocated in a program. In this case, the boolean captures whether some event should take place in the future. In this example an appropriate temporal sense is missing from the name. A name like allocated does not provide enough information and naming it is allocated does not make logical sense in the context of the program. A solution to this naming problem is to change the identi?er to should be allocated, which includes the necessary temporal sense communicating that this boolean is a ?ag for something expected to happen in the future. References [1] S. L. Abebe and P. Tonella. Natural language parsing of program element names for concept extraction. In 18th IEEE International Conference on Program Comprehension. IEEE, 2010. [2] K. Atkinson. Spell checking oriented word lists (scowl). [3] E. Boschee, R. Weischedel, and A. Zamanian. Automatic information extraction. In Proceedings of the International Conference on Intelligence Analysis, 2005. [4] B. Caprile and P. Tonella. Restructuring program identi?er names. In ICSM, 2000. [5] ML Collard, HH Kagdi, and JI Maletic. An XML-based lightweight C++ fact extractor. Program Comprehension, 2003. 11th IEEE International Workshop on, pages 134–143, 2003. [6] E. Hà ¸st and B. Østvold. The programmer’s lexicon, volume i: The verbs. In International Working Conference on Source Code Analysis and Manipulation, Beijing, China, September 2008. [7] E. W. Hà ¸st and B. M. Østvold. Debugging method names. In ECOOP 09. Springer Berlin / Heidelberg, 2009. [8] J. Jiang and C. Zhai. Instance weighting for domain adaptation in nlp. In ACL 2007, 2007. [9] D. Lawrie, D. Binkley, and C. Morrell. Normalizing source code vocabulary. In Proceedings of the 17th Working Conference on Reverse Engineering, 2010. [10] L. Shen, G. Satta, and A. K. Joshi. Guided learning for bidirectional sequence classi?cation. In ACL 07. ACL, June 2007. [11] D. Shepherd, Z. P. Fry, E. Hill, L. Pollock, and K. Vijay-Shanker. Using natural language program analysis to locate and understand action-oriented conerns. In AOSD 07. ACM, March 2007. [12] K. Toutanova, D. Klein, C. Manning, and Y. Singer. Feature-rich part-of-speech tagging with a cyclic dependency network. In HLTNAACL 2003, 2003. 4 Related Work This section brie?y reviews three projects that use POS information. Each uses an off-the-shelf POS tagger or lookup table. First, Host et al. study naming of Java methods using a lookup table to assign POS tags [7]. Their aim is to ?nd what they call â€Å"naming bugs† by checking to see if the method’s implementation is properly indicated with the name of the method. Second, Abebe and Tonella study class, method, and attribute names using a POS tagger based on a modi?cation of minipar to formulate domain concepts [1]. Nouns in the identi?ers are examined to form ontological relations between concepts. Based on a case study, their approach improved concept searching. Finally, Shepherd et al. considered ?nding concepts in code using natural language information [11]. The resulting Find-Concept tool locates action-oriented concerns more effectively than the other tools and with less user effort. This is made possible by POS information applied to source code. 4 How to cite Part of Speech Recognizer, Papers

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